MDD TWAS
This documents an MDD TWAS study. The analysis was carried out by Lorenza Dall’Aglio and Oliver Pain, with supervision from Cathryn Lewis.
This study used GWAS summary statistics from the Wray et al Major Depression GWAS, including 23andMe participants. We used SNP-weights derived by FUSION for brain tissues, HPA tissues, HPT tissues, and blood.
After reviewer comments, we also included SNP-weights derived by the PsychENCODE team for the DLPFC. The PsychENCODE SNP-weights were derived using all HRC imputed variants within the PsychENCODE dataset. The standard FUSION LD reference is restricted to HapMap3 variants, so when using the PsychENCODE SNP-weight we used an unrestricted version of the 1KG Phase 3 reference to improve SNP overlap.
1 Estimating transcriptome-wide significance threshold
First we need to estimate our transcriptome-wide significance threshold to account for the number of features tested. We will use a permutation-based approach to determine the appropriate transcriptome-wide significance threshold. It works by performing TWAS using the relevent SNP-weight panels.
Show a list of SNP-weight panels in the TWAS
| Panel |
|---|
| Adrenal_Gland |
| Brain_Amygdala |
| Brain_Anterior_cingulate_cortex_BA24 |
| Brain_Caudate_basal_ganglia |
| Brain_Cerebellar_Hemisphere |
| Brain_Cerebellum |
| Brain_Cortex |
| Brain_Frontal_Cortex_BA9 |
| Brain_Hippocampus |
| Brain_Hypothalamus |
| Brain_Nucleus_accumbens_basal_ganglia |
| Brain_Putamen_basal_ganglia |
| Brain_Substantia_nigra |
| CMC.BRAIN.RNASEQ |
| CMC.BRAIN.RNASEQ_SPLICING |
| NTR.BLOOD.RNAARR |
| Pituitary |
| Thyroid |
| Whole_Blood |
| YFS.BLOOD.RNAARR |
| PsychENCODE |
Generate null distribution
mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig
for batch in $(seq 1 20); do
sbatch -p brc,shared -n 1 --mem=10G /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASPermuThr.R \
--nperm 50 \
--ncore 1 \
--weights /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/Batch${batch}
doneEstimate transcriptome-wide significance threshold
library(data.table)
library(MKmisc)
# Create list of files containing minimum p values
batches<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',pattern='Batch*')
# Combine all the minimum p-values
min_P_all<-NULL
for(batch in batches){
min_P_all<-c(min_P_all,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',batch), header=F)$V1)
}
# Calculate the 5th percentile of the minimum p-values
TWalpha<-MKmisc::quantileCI(x=min_P_all, prob=0.05, method="exact",conf.level=0.95)
TWalpha # 1.368572e-06
# Calculate the 0.1th percentile which will be needed for the high-confidence associations section
TWalpha_001<-MKmisc::quantileCI(x=min_P_all, prob=0.001, method="exact",conf.level=0.99)
TWalpha_001 # 3.685926e-08
# Save the R object for future reference
saveRDS(TWalpha,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_05.RDS')
saveRDS(TWalpha_001,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_001.RDS')2 TWAS
2.1 FUSION SNP-weights
2.1.1 GWAS summary statistics preparation
GWAS summary statistics were munged using the LDSC munge_sumstats.py.
Show munge log file
## *********************************************************************
## * LD Score Regression (LDSC)
## * Version 1.0.0
## * (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane
## * Broad Institute of MIT and Harvard / MIT Department of Mathematics
## * GNU General Public License v3
## *********************************************************************
## Call:
## ./munge_sumstats.py \
## --N-con-col Ncon \
## --out /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01 \
## --merge-alleles /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist \
## --N-cas-col Ncas \
## --N-col N \
## --info-min 0.6 \
## --sumstats /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz
##
## Interpreting column names as follows:
## INFO: INFO score (imputation quality; higher --> better imputation)
## SNP: Variant ID (e.g., rs number)
## N: Sample size
## A1: Allele 1, interpreted as ref allele for signed sumstat.
## P: p-Value
## A2: Allele 2, interpreted as non-ref allele for signed sumstat.
## Ncon: Number of controls
## Ncas: Number of cases
## FREQ: Allele frequency
## OR: Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
##
## Reading list of SNPs for allele merge from /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist
## Read 1217311 SNPs for allele merge.
## Reading sumstats from /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz into memory 5000000.0 SNPs at a time.
## Read 10155339 SNPs from --sumstats file.
## Removed 8953119 SNPs not in --merge-alleles.
## Removed 0 SNPs with missing values.
## Removed 0 SNPs with INFO <= 0.6.
## Removed 17182 SNPs with MAF <= 0.01.
## Removed 0 SNPs with out-of-bounds p-values.
## Removed 0 variants that were not SNPs or were strand-ambiguous.
## 1185038 SNPs remain.
## Removed 0 SNPs with duplicated rs numbers (1185038 SNPs remain).
## Removed 0 SNPs with N < 307422.666667 (1185038 SNPs remain).
## Median value of OR was 1.0, which seems sensible.
## Removed 0 SNPs whose alleles did not match --merge-alleles (1185038 SNPs remain).
## Writing summary statistics for 1217311 SNPs (1185038 with nonmissing beta) to /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz.
##
## Metadata:
## Mean chi^2 = 1.534
## Lambda GC = 1.419
## Max chi^2 = 79.045
## 597 Genome-wide significant SNPs (some may have been removed by filtering).
##
## Conversion finished at Wed Feb 14 12:45:03 2018
## Total time elapsed: 2.0m:19.39sThen, I modified the sumstats to remove the rows with missing values.
Show code
module add general/R/3.5.0
R
library(data.table)
# Read in the LDSC munged sumstats using zcat to unzip the file
sumstats<-data.frame(fread('zcat /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz'))
# Remove rows containing NA values
sumstats<-sumstats[complete.cases(sumstats),]
# Save the reformatted sumstats and compress
write.table(sumstats, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA', col.names=T, row.names=F,quote=F)
q()
n
gzip /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA
2.1.2 TWAS analysis
Run TWAS
# Run analysis for each chromomsome and each panel
for chr in $(seq 1 22); do
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
qsub -cwd /mnt/lustre/users/k1894478/scripts/Rscript_correct.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
--sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA.gz \
--weights /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights}/${weights}.pos \
--weights_dir /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights} \
--ref_ld_chr /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/LDREF/1000G.EUR. \
--out /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr${chr} \
--chr ${chr} \
--coloc_P 4.432625e-06 \
--GWASN 480359
done
done
# Check each chromosome finished
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
echo $weights
ls /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* | wc -l #this tells you how many files there are with the name test.X.chrX
done
# To combine per chromosome results file, without duplicating the header
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
head -n 1 /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr1 > /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW
tail -n +2 -q /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* >> /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW
done
# Create file containing results for all tissues
awk '
FNR==1 && NR!=1 { while (/TWAS.P/) getline; }
1 {print}
' /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.GW >/mnt/lustre/users/k1894478/output_correct/wray_output/AllTissues.GW
# Check the .GW files which were created
ls *.GW
# Delete the per chromosome files
for chr in $(seq 1 22); do
rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr${chr}
done
rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr6.MHC2.2 PsychENCODE SNP-weights
2.2.1 GWAS summary statistics preparation
The PsychENCODE SNP-weights are not restricted to HapMap3 variants, so we need to use unrestricted GWAS summary statistics also. To achieve this I use the FOCUS software munge script.
Show code
# Edit BP column name
zcat /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz | sed -e 's/ORIGBP/BP/g' > /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP
# Munge
/users/k1806347/brc_scratch/Software/focus.sh munge /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus
# Delete temporary file
rm /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BPShow munge log file
## ===================================
## FOCUS v0.6.10
## ===================================
## focus munge
## /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP
## --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus
##
## Starting log...
## [2019-11-14 10:54:16 - INFO] Interpreting column names as follows:
## [2019-11-14 10:54:16 - INFO] SNP: Variant ID (e.g., rs number)
## [2019-11-14 10:54:16 - INFO] CHR: Chromsome
## [2019-11-14 10:54:16 - INFO] BP: Base position
## [2019-11-14 10:54:16 - INFO] A1: Allele 1, interpreted as ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] A2: Allele 2, interpreted as non-ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] P: p-Value
## [2019-11-14 10:54:16 - INFO] INFO: INFO score (imputation quality; higher --> better imputation)
## [2019-11-14 10:54:16 - INFO] OR: Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
## [2019-11-14 10:54:16 - INFO] N: Sample size
## [2019-11-14 10:54:16 - INFO] Reading sumstats from /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP into memory 5000000 SNPs at a time
## [2019-11-14 10:54:24 - INFO] Reading SNP chunk 1
## [2019-11-14 10:54:44 - INFO] Reading SNP chunk 2
## [2019-11-14 10:54:52 - INFO] Reading SNP chunk 3
## [2019-11-14 10:54:52 - INFO] Done reading SNP chunks
## [2019-11-14 10:54:58 - INFO] Read 10155339 SNPs from --sumstats file
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with missing values
## [2019-11-14 10:54:58 - INFO] Removed 2561651 SNPs with INFO <= 0.9
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with MAF <= 0.01
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with out-of-bounds p-values
## [2019-11-14 10:54:58 - INFO] Removed 1656695 variants that were not SNPs or were strand-ambiguous
## [2019-11-14 10:54:58 - INFO] 5936993 SNPs remain
## [2019-11-14 10:55:04 - INFO] Removed 0 SNPs with duplicated rs numbers (5936993 SNPs remain).
## [2019-11-14 10:55:06 - INFO] Removed 188 SNPs with N < 307422.6666666667 (5936805 SNPs remain)
## [2019-11-14 10:56:30 - INFO] Median value of OR was 1.0, which seems sensible.
## [2019-11-14 10:56:31 - INFO] Writing summary statistics for 5936805 SNPs (5936805 with nonmissing beta) to /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz.
## [2019-11-14 10:57:53 - INFO] METADATA - Mean chi^2 = 1.492
## [2019-11-14 10:57:54 - INFO] METADATA - Lambda GC = 1.383
## [2019-11-14 10:57:54 - INFO] METADATA - Max chi^2 = 79.045
## [2019-11-14 10:57:54 - INFO] METADATA - 2960 Genome-wide significant SNPs (some may have been removed by filtering)
## [2019-11-14 10:57:54 - INFO] Conversion finished2.2.2 TWAS analysis
Run TWAS
for chr in $(seq 1 22); do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
--sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
--weights /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos \
--weights_dir /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights \
--ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
--out /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr${chr} \
--chr ${chr} \
--coloc_P 4.432625e-06 \
--GWASN 480359
doneFormat to match FUSION panel results
library(data.table)
psychENCODE<-NULL
for(i in 1:22){
if(i == 6){
tmp1<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
tmp2<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i,'.MHC'))
tmp<-rbind(tmp1,tmp2)
} else {
tmp<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
}
psychENCODE<-rbind(psychENCODE, tmp)
}
col_order<-names(psychENCODE)
psychENCODE$PANEL<-as.character(psychENCODE$PANEL)
psychENCODE$PANEL<-'PsychENCODE'
library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)
psychENCODE<-merge(psychENCODE, Genes, by.x='ID', by.y='ensembl_gene_id')
psychENCODE$ID<-psychENCODE$external_gene_name
psychENCODE$external_gene_name<-NULL
psychENCODE<-psychENCODE[,col_order, with=F]
write.table(psychENCODE, '/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW', col.names=T, row.names=F, quote=F)2.3 Combine results across FUSION and PsychENCODE panels
Format to match FUSION panel results
library(data.table)
psych<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/AllTissues.GW')
all<-rbind(psych,fusion)
# Write out full results
write.table(all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt', row.names=F, col.names=T, quote=F)
# Write out transcriptome-wide significant results
write.table(all[which(all$TWAS.P < 1.368572e-06),], '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt', row.names=F, col.names=T, quote=F)
dim(all[which(all$TWAS.P < 1.368572e-06),]) # 176 hits
length(unique(all[which(all$TWAS.P < 1.368572e-06),]$ID)) # 94 unique genes
# Check how much PsychENCODE adds
dim(fusion[which(fusion$TWAS.P < 1.368572e-06),]) # 154 hits
length(unique(fusion[which(fusion$TWAS.P < 1.368572e-06),]$ID)) # 84 unique genes3 Post-TWAS
3.1 Create Manahattan-style plots
Show code
# Manhattan plot based on permutation significance
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 1.368572e-06 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_Manhattan \
--width 3500 \
--height 2500
#Manhattan plot for high confidence associations
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 3.685926e-08 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_HighConf_Manhattan \
--width 3500 \
--height 2500
Show Manhattan plots
MDD TWAS Manhattan Plot with transcriptome-wide signficance
MDD TWAS Manhattan Plot with high-confidence threshold
3.2 Conditional analysis
Run post_process.R script
# Change directory to location of glist-hg19 file
cd /users/k1806347/brc_scratch/Data/Gene_Locations
mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional
for chr in $(seq 1 22); do
status=$(awk -v var="${chr}" '$4 == var {print "Present";exit;}' /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/post-TWAS/MDD_TWAS_AllTissues_TWSig.txt )
if [ "$status" == "Present" ]; then
sbatch -p brc,shared --mem 25G -n 1 /users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.post_process.R \
--input /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt \
--sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
--report \
--ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
--out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/test.cond.chr${chr} \
--chr ${chr} \
--plot \
--plot_legend all \
--save_loci \
--locus_win 500000
fi
done3.3 Process TWAS results
Clean the TWAS results
###
# Clean file PANEL names
###
rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt")
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt")
twas_sign$BEST.GWAS.P<-2*pnorm(-abs(twas_sign$BEST.GWAS.Z))
sum(twas_sign$BEST.GWAS.P > 5e-8) # 63
sum(twas_sign$BEST.GWAS.P < 5e-8) # 113
str(twas_sign)
str(twas)
#clean the PANEL names of the output df containing results on all tested features
twas$PANEL_clean<-gsub('_',' ',twas$PANEL)
twas$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas$PANEL_clean)
twas$PANEL_clean<-gsub('SPLICING','Splicing',twas$PANEL_clean)
twas$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas$PANEL_clean)
twas$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas$PANEL_clean)
twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)]<-paste0('GTEx ',twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas$PANEL_clean<-gsub('Brain', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas$PANEL_clean)
twas$PANEL_clean <- gsub('basal ganglia', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA9', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA24', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub(' ', ' ', twas$PANEL_clean)
# Shorten panel name to plot easily
twas$PANEL_clean_short<-substr(twas$PANEL_clean, start = 1, stop = 25) #start the name at the first character and stop at the 25th
twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25]<-paste0(twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25], "...")
#do the same for the output file with sign features only
twas_sign$PANEL_clean<-gsub('_',' ',twas_sign$PANEL)
twas_sign$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('SPLICING','Splicing',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)]<-paste0('GTEx ',twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas_sign$PANEL_clean<-gsub('Brain', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('basal ganglia', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA9', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA24', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub(' ', ' ', twas_sign$PANEL_clean)
# Shorten panel name to plot easily
twas_sign$PANEL_clean_short<-substr(twas_sign$PANEL_clean, start = 1, stop = 25) #start the name at the first character and stop at the 25th
twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25]<-paste0(twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25], "...")
#check the variables
str(twas)
str(twas_sign)
###
# Deal with missingness and subset for the relevant cols only
###
##TWAS df
#exclude missings
twas<-twas[!is.na(twas$TWAS.Z),]
twas<-twas[!is.na(twas$TWAS.P),]
#subset columns needed
twas_sub <- twas[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sub)
##TWAS sign df
#exclude missings
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.Z),]
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.P),]
#subset columns needed
twas_sign_sub <- twas_sign[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sign_sub)
###
# Update positions
###
# Rationale: the positions in the output files created by FUSION are rounded, thus not completely accurate.
# Therefore, we need to update the positions (P0 and P1) based on the pos files in Rosalind. This needs to be done on Putty though. A new file will be saved and reopened here.
setwd('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/')
weights<-c('Adrenal_Gland', 'Brain_Amygdala', 'Brain_Anterior_cingulate_cortex_BA24', 'Brain_Caudate_basal_ganglia', 'Brain_Cerebellar_Hemisphere', 'Brain_Cerebellum', 'Brain_Cortex', 'Brain_Frontal_Cortex_BA9', 'Brain_Hippocampus', 'Brain_Hypothalamus', 'Brain_Nucleus_accumbens_basal_ganglia', 'Brain_Putamen_basal_ganglia', 'Brain_Substantia_nigra', 'CMC.BRAIN.RNASEQ', 'CMC.BRAIN.RNASEQ_SPLICING', 'NTR.BLOOD.RNAARR', 'Pituitary', 'Thyroid', 'Whole_Blood', 'YFS.BLOOD.RNAARR')
#Get all pos files within the SNP-weight sets and bind them
FUSION_pos<-NULL
for(i in weights){
FUSION_pos_temp<-read.table(paste(i, '/',i, '.pos',sep=''), header=T, stringsAsFactors=F) #repeating i twice with / in the middle is to get one folder further
FUSION_pos<-rbind(FUSION_pos, FUSION_pos_temp)
}
PsychENCODE_pos<-read.table('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos', header=T, stringsAsFactors=F)
PsychENCODE_pos$PANEL<-'PsychENCODE'
# Combine pos files
FUSION_pos<-rbind(FUSION_pos, PsychENCODE_pos)
write.table(FUSION_pos,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos', col.names=T, row.names=F, quote=F)
str(FUSION_pos) #97733 observations of 7 variables (PANEL, WGT, ID, CHR, P0, P1, N)
###
# Merge the pos file with the twas_sub and twas_sign_sub df
###
#the pos file and the output file do not have the same columns with the same information. We therefore need to slightly modify the TWAS columns
twas_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sub$FILE)
twas_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sub$tmp)
#to delete the full pathway of the file and just keep the important information
twas_sub$PANEL<-sub('/.*','', twas_sub$tmp)
twas_sub$Feature<-gsub('.*/','',twas_sub$tmp)
twas_sub$WGT<-paste0(twas_sub$PANEL, '/', twas_sub$Feature)
twas_sub$PANEL<-NULL
twas_sub$tmp<-NULL
twas_sub$Feature<-NULL
twas_sub[order(twas_sub$WGT), ]
FUSION_pos[order(FUSION_pos$WGT), ]
#merge
twas_sub_correct <- merge(twas_sub, FUSION_pos, by="WGT")
#check
head(twas_sub_correct)
#clean
twas_sub_correct$ID.y<-NULL
colnames(twas_sub_correct)
names(twas_sub_correct)[3]<-'ID' #to change the name of IDx to ID
head(twas_sub_correct)
#repeat everything for the twas_sign_sub file
twas_sign_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sign_sub$FILE)
twas_sign_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sign_sub$tmp)
#to delete the full pathway of the file and just keep the important information
twas_sign_sub$PANEL<-sub('/.*','', twas_sign_sub$tmp)
twas_sign_sub$Feature<-gsub('.*/','',twas_sign_sub$tmp)
twas_sign_sub$WGT<-paste0(twas_sign_sub$PANEL, '/', twas_sign_sub$Feature)
twas_sign_sub$PANEL<-NULL
twas_sign_sub$tmp<-NULL
twas_sign_sub$Feature<-NULL
# twas_sign_sub[order(twas_sign_sub$WGT), ]
#merge
twas_sign_sub_correct <- merge(twas_sign_sub, FUSION_pos, by="WGT")
#check
head(twas_sign_sub_correct)
#clean
twas_sign_sub_correct$ID.y<-NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[3]<-'ID' #to change the name of IDx to ID
head(twas_sign_sub_correct)
dim(twas_sign_sub_correct)
dim(twas_sub_correct)
###
# Clean output files for future scripts
###
#clean both output files to have clean outputs to use in future scripts
#twas sign sub correct df
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[6] <- "CHR"
twas_sign_sub_correct$CHR.y <- NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[17] <- "P0" #turn POy into P0 - nb P0y is the one withh the more accurate positions
names(twas_sign_sub_correct)[18] <- "P1"
twas_sign_sub_correct$P0.x <- NULL
twas_sign_sub_correct$P1.x <- NULL
colnames(twas_sign_sub_correct)
twas_sign_sub_correct$N <- NULL
str(twas_sign_sub_correct)
#change the variable types for those which are wrong
#turn PO and P1 into numerical variables
twas_sign_sub_correct$P0 <- as.numeric(as.character(twas_sign_sub_correct$P0))
twas_sign_sub_correct$P1 <- as.numeric(as.character(twas_sign_sub_correct$P1))
str(twas_sign_sub_correct)
#twas_sub correct df
colnames(twas_sub_correct)
names(twas_sub_correct)[6]<- "CHR"
names(twas_sub_correct)[18]<- "P0"
names(twas_sub_correct)[19]<- "P1"
twas_sub_correct$CHR.y <- NULL
twas_sub_correct$P0.x <- NULL
twas_sub_correct$P1.x <- NULL
colnames(twas_sub_correct)
twas_sub_correct$N <- NULL
str(twas_sub_correct)
#change variable type for PO and P1
twas_sub_correct$P0 <- as.numeric(as.character(twas_sub_correct$P0))
twas_sub_correct$P1 <- as.numeric(as.character(twas_sub_correct$P1))
#save
write.table(twas_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", sep = " ", col.names = T, row.names = F)
write.table(twas_sign_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt", sep = " ", col.names = T, row.names=F)
q()
nCreate a table with the transcriptome-wide significant findings
rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
str(twas_sign)
twas_sign$CHR <- as.numeric(as.character(twas_sign$CHR))
twas_sign$P0 <- as.numeric(as.character(twas_sign$P0))
twas_sign$P1 <- as.numeric(as.character(twas_sign$P1))
twas_sign <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]
twas_sign$Location <- paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1)
colnames(twas_sign)
library(dplyr)
library(tibble)
twas_sign <- as_data_frame(twas_sign)
col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P")
twas_sign_final <- twas_sign[, col_order]
write.csv(twas_sign_final, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.brief.csv", row.names=F)
twas_sign_finalShow transcriptome-wide significant table
| Location | ID | PANEL_clean_short | TWAS.Z | TWAS.P |
|---|---|---|---|---|
| chr1:8412457-8877702 | RERE | GTEx Whole Blood | -5.095707 | 3.47e-07 |
| chr1:8412457-8877702 | RERE | YFS Blood | -5.310078 | 1.10e-07 |
| chr1:8484705-8494898 | RP5-1115A15.1 | GTEx Thyroid | -5.175240 | 2.28e-07 |
| chr1:8484705-8494898 | RP5-1115A15.1 | GTEx Whole Blood | -4.866386 | 1.14e-06 |
| chr1:36884051-36884179 | SNORA63 | GTEx Nucleus accumbens | 4.848870 | 1.24e-06 |
| chr1:71861623-72748417 | NEGR1 | GTEx Caudate | 5.780100 | 7.47e-09 |
| chr1:71861623-72748417 | NEGR1 | GTEx Putamen | 5.548510 | 2.88e-08 |
| chr1:71861623-72748417 | NEGR1 | GTEx Whole Blood | 8.760622 | 1.94e-18 |
| chr1:72767155-72767512 | RPL31P12 | GTEx Cerebellar Hemispher… | -7.785520 | 6.94e-15 |
| chr1:72767155-72767512 | RPL31P12 | GTEx Cerebellum | -7.708820 | 1.27e-14 |
| chr1:72767155-72767512 | RPL31P12 | PsychENCODE | -7.742756 | 9.73e-15 |
| chr1:175873898-175889649 | RP11-318C24.2 | GTEx Thyroid | -5.027510 | 4.97e-07 |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | -4.958690 | 7.10e-07 |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | 5.039850 | 4.66e-07 |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | -5.005960 | 5.56e-07 |
| chr1:181452685-181775921 | CACNA1E | CMC DLPFC Splicing | -4.989390 | 6.06e-07 |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC | 4.848374 | 1.24e-06 |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC Splicing | -5.421950 | 5.90e-08 |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC Splicing | 5.018050 | 5.22e-07 |
| chr2:58386377-58468515 | FANCL | CMC DLPFC | -5.183180 | 2.18e-07 |
| chr2:58386377-58468515 | FANCL | CMC DLPFC Splicing | 4.897476 | 9.71e-07 |
| chr2:197831741-198175897 | ANKRD44 | YFS Blood | -5.690140 | 1.27e-08 |
| chr2:198254508-198299815 | SF3B1 | GTEx Hypothalamus | 5.214900 | 1.84e-07 |
| chr3:44481261-44561226 | ZNF445 | CMC DLPFC | -5.103280 | 3.34e-07 |
| chr4:41937137-41962589 | TMEM33 | PsychENCODE | 4.837418 | 1.32e-06 |
| chr4:41983713-41988476 | DCAF4L1 | GTEx Thyroid | -5.128000 | 2.93e-07 |
| chr4:41990758-41991254 | RP11-814H16.2 | GTEx Cerebellar Hemispher… | 5.009600 | 5.45e-07 |
| chr4:41992489-42092474 | SLC30A9 | GTEx Amygdala | -5.253400 | 1.49e-07 |
| chr4:41992489-42092474 | SLC30A9 | GTEx ACC | -5.001690 | 5.68e-07 |
| chr4:41992489-42092474 | SLC30A9 | GTEx Caudate | -4.854800 | 1.21e-06 |
| chr4:41992489-42092474 | SLC30A9 | GTEx Cortex | -5.774530 | 7.72e-09 |
| chr4:41992489-42092474 | SLC30A9 | GTEx Hypothalamus | -5.085140 | 3.67e-07 |
| chr4:41992489-42092474 | SLC30A9 | GTEx Nucleus accumbens | -5.602700 | 2.11e-08 |
| chr4:41992489-42092474 | SLC30A9 | PsychENCODE | -5.259200 | 1.45e-07 |
| chr5:87564712-87732502 | TMEM161B-AS1 | PsychENCODE | 6.091010 | 1.12e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Adrenal Gland | 5.360090 | 8.32e-08 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Amygdala | 6.118500 | 9.45e-10 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx ACC | 6.445500 | 1.15e-10 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Caudate | 6.282167 | 3.34e-10 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cerebellar Hemispher… | 6.011700 | 1.84e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cerebellum | 6.053050 | 1.42e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cortex | 6.021420 | 1.73e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Frontal Cortex | 6.720000 | 1.82e-11 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Hypothalamus | 5.875800 | 4.21e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Nucleus accumbens | 6.010490 | 1.85e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Putamen | 6.372050 | 1.87e-10 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Substantia nigra | 6.057270 | 1.38e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Pituitary | 6.048500 | 1.46e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Thyroid | 5.889760 | 3.87e-09 |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Whole Blood | 5.526440 | 3.27e-08 |
| chr5:87729709-87794514 | CTC-498M16.4 | GTEx Substantia nigra | 5.403610 | 6.53e-08 |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx ACC | -5.813700 | 6.11e-09 |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Cerebellar Hemispher… | -5.861000 | 4.60e-09 |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Cortex | -6.510990 | 7.47e-11 |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Frontal Cortex | -7.091600 | 1.33e-12 |
| chr5:87988462-87989789 | CTC-467M3.3 | PsychENCODE | -6.097890 | 1.07e-09 |
| chr5:140024947-140027370 | NDUFA2 | CMC DLPFC | 5.190020 | 2.10e-07 |
| chr5:140201222-140203811 | PCDHA5 | GTEx Thyroid | -5.402970 | 6.55e-08 |
| chr5:140220907-140223351 | PCDHA8 | GTEx Cerebellar Hemispher… | -4.980100 | 6.36e-07 |
| chr6:26188921-26189323 | HIST1H4D | NTR Blood | -4.987600 | 6.11e-07 |
| chr6:26365386-26378540 | BTN3A2 | NTR Blood | 5.326600 | 1.00e-07 |
| chr6:26365387-26378546 | BTN3A2 | GTEx Cerebellar Hemispher… | 5.188200 | 2.12e-07 |
| chr6:26365387-26378546 | BTN3A2 | GTEx Hippocampus | 4.963000 | 6.96e-07 |
| chr6:26365387-26378546 | BTN3A2 | GTEx Pituitary | 5.898930 | 3.66e-09 |
| chr6:26365387-26378546 | BTN3A2 | GTEx Thyroid | 5.481600 | 4.22e-08 |
| chr6:26365387-26378546 | BTN3A2 | GTEx Whole Blood | 5.086960 | 3.64e-07 |
| chr6:26538633-26546482 | HMGN4 | GTEx Cerebellum | 5.395400 | 6.84e-08 |
| chr6:27215480-27224250 | PRSS16 | GTEx Cerebellar Hemispher… | -4.891200 | 1.00e-06 |
| chr6:27215480-27224250 | PRSS16 | GTEx Cerebellum | -4.947900 | 7.50e-07 |
| chr6:27215480-27224250 | PRSS16 | GTEx Frontal Cortex | -5.045000 | 4.54e-07 |
| chr6:27215480-27224250 | PRSS16 | GTEx Pituitary | -5.916080 | 3.30e-09 |
| chr6:27215480-27224250 | PRSS16 | GTEx Whole Blood | -5.335920 | 9.51e-08 |
| chr6:27325604-27339304 | ZNF204P | GTEx Adrenal Gland | -5.032700 | 4.84e-07 |
| chr6:27371789-27374743 | RP1-153G14.4 | GTEx Hippocampus | 5.354000 | 8.60e-08 |
| chr6:27418522-27440897 | ZNF184 | GTEx Caudate | -6.325200 | 2.53e-10 |
| chr6:27418522-27440897 | ZNF184 | GTEx Hypothalamus | -4.952200 | 7.34e-07 |
| chr6:27840926-27841289 | HIST1H4L | NTR Blood | 4.870800 | 1.11e-06 |
| chr6:28058932-28061442 | ZSCAN12P1 | PsychENCODE | 6.268010 | 3.66e-10 |
| chr6:28058932-28061442 | ZSCAN12P1 | GTEx Whole Blood | -4.936930 | 7.94e-07 |
| chr6:28083406-28084329 | RP1-265C24.5 | GTEx Hippocampus | 5.532000 | 3.16e-08 |
| chr6:28092338-28097860 | ZSCAN16 | YFS Blood | -6.109000 | 1.00e-09 |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Cerebellum | -5.307800 | 1.11e-07 |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Hippocampus | -6.017000 | 1.77e-09 |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Pituitary | -6.159020 | 7.32e-10 |
| chr6:28227098-28228736 | NKAPL | PsychENCODE | 5.002860 | 5.65e-07 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Adrenal Gland | 5.094600 | 3.49e-07 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Cerebellum | 5.062800 | 4.13e-07 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Hippocampus | 5.198000 | 2.01e-07 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Putamen | 5.739000 | 9.52e-09 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Thyroid | 5.338400 | 9.38e-08 |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Whole Blood | 5.662330 | 1.49e-08 |
| chr6:28249314-28270326 | PGBD1 | GTEx Cerebellar Hemispher… | -6.313100 | 2.74e-10 |
| chr6:28292470-28324048 | ZSCAN31 | GTEx Amygdala | -5.084150 | 3.69e-07 |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Amygdala | 4.949900 | 7.43e-07 |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Hippocampus | 4.951000 | 7.37e-07 |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Thyroid | 6.093300 | 1.11e-09 |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Hypothalamus | -5.777500 | 7.58e-09 |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Putamen | -4.894000 | 9.90e-07 |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Pituitary | -4.953290 | 7.30e-07 |
| chr6:30644166-30655672 | PPP1R18 | GTEx Adrenal Gland | 4.910200 | 9.10e-07 |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | -5.299700 | 1.16e-07 |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | -5.067100 | 4.04e-07 |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | 4.936600 | 7.95e-07 |
| chr6:30695486-30710510 | FLOT1 | GTEx Cerebellum | -5.299000 | 1.16e-07 |
| chr6:30695486-30710510 | FLOT1 | GTEx Pituitary | -5.253270 | 1.49e-07 |
| chr6:30695486-30710510 | FLOT1 | GTEx Thyroid | -5.557400 | 2.74e-08 |
| chr6:30881982-30894236 | VARS2 | GTEx Cortex | 5.922000 | 3.18e-09 |
| chr6:30881982-30894236 | VARS2 | GTEx Whole Blood | 6.323130 | 2.56e-10 |
| chr6:31255287-31256741 | WASF5P | GTEx Pituitary | -5.156240 | 2.52e-07 |
| chr6:31368479-31445283 | HCP5 | GTEx Thyroid | 6.400800 | 1.55e-10 |
| chr6:31462658-31478901 | MICB | GTEx Thyroid | -5.557000 | 2.74e-08 |
| chr6:31606805-31620482 | BAG6 | CMC DLPFC Splicing | -5.580000 | 2.40e-08 |
| chr6:31694815-31698357 | DDAH2 | GTEx Frontal Cortex | 5.409500 | 6.32e-08 |
| chr6:31694816-31698039 | DDAH2 | CMC DLPFC | 5.344500 | 9.07e-08 |
| chr6:99817347-99842082 | COQ3 | CMC DLPFC Splicing | 5.146560 | 2.65e-07 |
| chr6:105404922-105531207 | LIN28B | CMC DLPFC | -5.232050 | 1.68e-07 |
| chr6:105404923-105531207 | LIN28B | PsychENCODE | -5.105689 | 3.30e-07 |
| chr6:105584224-105617820 | BVES-AS1 | GTEx Amygdala | -5.578300 | 2.43e-08 |
| chr7:12250867-12282993 | TMEM106B | GTEx Adrenal Gland | 5.505026 | 3.69e-08 |
| chr7:12250867-12282993 | TMEM106B | PsychENCODE | -5.790690 | 7.01e-09 |
| chr7:12250867-12282993 | TMEM106B | GTEx Whole Blood | 5.531000 | 3.18e-08 |
| chr7:12250867-12276886 | TMEM106B | YFS Blood | 5.373600 | 7.72e-08 |
| chr7:24836158-25021253 | OSBPL3 | GTEx Pituitary | -5.622890 | 1.88e-08 |
| chr8:52232136-52722005 | PXDNL | CMC DLPFC | 5.887460 | 3.92e-09 |
| chr8:61297147-61429354 | RP11-163N6.2 | GTEx Thyroid | -5.336530 | 9.47e-08 |
| chr9:126605315-126605965 | PIGFP2 | PsychENCODE | -5.305600 | 1.12e-07 |
| chr11:57067112-57092426 | TNKS1BP1 | GTEx Adrenal Gland | 4.922610 | 8.54e-07 |
| chr11:57405497-57420263 | AP000662.4 | GTEx Thyroid | -4.980256 | 6.35e-07 |
| chr11:57424488-57429340 | CLP1 | GTEx Whole Blood | 5.195860 | 2.04e-07 |
| chr11:61535973-61560274 | TMEM258 | PsychENCODE | 5.021730 | 5.12e-07 |
| chr11:113280318-113346111 | DRD2 | GTEx Frontal Cortex | -5.073787 | 3.90e-07 |
| chr13:53602875-53626196 | OLFM4 | CMC DLPFC | 5.091290 | 3.56e-07 |
| chr14:42057064-42074059 | CTD-2298J14.2 | GTEx Thyroid | -5.678860 | 1.36e-08 |
| chr14:42076773-42373752 | LRFN5 | GTEx Cerebellar Hemispher… | 5.423400 | 5.85e-08 |
| chr14:42076773-42373752 | LRFN5 | GTEx Cerebellum | 5.597540 | 2.17e-08 |
| chr14:59951161-59971429 | JKAMP | GTEx Thyroid | -5.125100 | 2.97e-07 |
| chr14:59971779-60043549 | CCDC175 | GTEx Thyroid | -5.478850 | 4.28e-08 |
| chr14:60062693-60337557 | RTN1 | CMC DLPFC Splicing | -4.874920 | 1.09e-06 |
| chr14:60062695-60337684 | RTN1 | GTEx Thyroid | -5.348450 | 8.87e-08 |
| chr14:64319682-64693151 | SYNE2 | NTR Blood | 5.609528 | 2.03e-08 |
| chr14:64550950-64770377 | ESR2 | GTEx Pituitary | -5.982300 | 2.20e-09 |
| chr14:64550950-64770377 | ESR2 | GTEx Whole Blood | -5.655371 | 1.56e-08 |
| chr14:75120140-75179818 | AREL1 | PsychENCODE | -5.015110 | 5.30e-07 |
| chr14:75319736-75330537 | PROX2 | GTEx Thyroid | -5.758100 | 8.51e-09 |
| chr14:75348593-75370450 | DLST | CMC DLPFC | 4.981400 | 6.31e-07 |
| chr14:75348594-75370448 | DLST | PsychENCODE | 5.089700 | 3.59e-07 |
| chr14:75370656-75389188 | RPS6KL1 | CMC DLPFC Splicing | -5.023810 | 5.07e-07 |
| chr14:75370657-75390099 | RPS6KL1 | PsychENCODE | -4.952550 | 7.32e-07 |
| chr14:103878456-103879098 | RP11-600F24.2 | PsychENCODE | 5.185660 | 2.15e-07 |
| chr14:103985996-103989448 | CKB | YFS Blood | 5.346000 | 8.99e-08 |
| chr14:103995508-104003410 | TRMT61A | CMC DLPFC | 5.051300 | 4.39e-07 |
| chr14:103995521-104003410 | TRMT61A | GTEx Whole Blood | 4.977593 | 6.44e-07 |
| chr14:104019758-104028214 | RP11-894P9.2 | GTEx Thyroid | -5.462560 | 4.69e-08 |
| chr14:104153913-104154464 | RP11-73M18.6 | PsychENCODE | 5.031320 | 4.87e-07 |
| chr14:104160897-104161507 | RP11-73M18.7 | PsychENCODE | 4.856130 | 1.20e-06 |
| chr14:104162690-104163500 | RP11-73M18.8 | GTEx Amygdala | 5.142000 | 2.72e-07 |
| chr14:104177607-104179149 | AL049840.1 | GTEx Cerebellum | 5.029540 | 4.92e-07 |
| chr14:104177607-104179149 | AL049840.1 | GTEx Cortex | 5.143620 | 2.69e-07 |
| chr14:104179904-104180441 | RP11-73M18.9 | GTEx Cortex | 4.977330 | 6.45e-07 |
| chr14:104179904-104180586 | RP11-73M18.9 | PsychENCODE | 4.830100 | 1.36e-06 |
| chr16:72146056-72210777 | PMFBP1 | PsychENCODE | -5.160620 | 2.46e-07 |
| chr17:27400528-27507430 | MYO18A | GTEx Adrenal Gland | -5.128570 | 2.92e-07 |
| chr17:27401933-27405875 | TIAF1 | GTEx Adrenal Gland | -5.361200 | 8.27e-08 |
| chr17:65520597-65521538 | CTD-2653B5.1 | PsychENCODE | 5.105730 | 3.30e-07 |
| chr18:52385091-52562747 | RAB27B | PsychENCODE | 5.012900 | 5.36e-07 |
| chr18:52495707-52562747 | RAB27B | CMC DLPFC Splicing | 4.843190 | 1.28e-06 |
| chr20:47835831-47860614 | DDX27 | CMC DLPFC | 4.836260 | 1.32e-06 |
| chr22:41165634-41215403 | SLC25A17 | GTEx Nucleus accumbens | 5.076990 | 3.83e-07 |
| chr22:41165634-41215403 | SLC25A17 | GTEx Thyroid | 4.896100 | 9.78e-07 |
| chr22:41253088-41351450 | XPNPEP3 | GTEx Frontal Cortex | 4.951000 | 7.38e-07 |
| chr22:41258260-41363888 | XPNPEP3 | CMC DLPFC | 5.110000 | 3.21e-07 |
| chr22:41487790-41576081 | EP300 | GTEx Cerebellum | 5.493900 | 3.93e-08 |
| chr22:41487790-41576081 | EP300 | YFS Blood | 5.059100 | 4.21e-07 |
| chr22:41641614-41682216 | RANGAP1 | CMC DLPFC Splicing | 5.240100 | 1.61e-07 |
| chr22:41641615-41682255 | RANGAP1 | PsychENCODE | -5.575273 | 2.47e-08 |
| chr22:41697526-41756151 | ZC3H7B | GTEx Cerebellum | 5.729100 | 1.01e-08 |
3.4 Plot TWAS results
Create QQ-plot and histogram of p-values
####
#QQplot
###
#load file with all hits - not just sign, ones
twas_sub_correct <- read.table("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", header=T, stringsAsFactors = F)
ggd.qqplot = function(pvector, main=NULL, ...) {
o = -log10(sort(pvector,decreasing=F))
e = -log10( 1:length(o)/length(o) )
plot(e,o,pch=19,cex=1, main=main, ...,
xlab=expression(Expected~~-log[10](italic(p))),
ylab=expression(Observed~~-log[10](italic(p))),
xlim=c(0,max(e)), ylim=c(0,max(o)))
lines(e,e,col="red")
}
pvalues <- twas_sub_correct$TWAS.P
# Add a title
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_QQplot.png",width = 2000, height = 2000, units = "px", res=300)
ggd.qqplot(pvalues, "QQ-plot of TWAS p-values")
dev.off()
###
# Histogram of p-values
###
library(ggplot2)
## HISTOGRAM OF P-VALUES
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_pValHist.png",width = 2000, height = 2000, units = "px", res=300)
hist(twas_sub_correct$TWAS.P,
main = "Histogram of TWAS p-values",
xlab ="P-values",
ylab = "Frequency")
dev.off()Show plots
MDD TWAS QQ-plot
As shown below in the QQ-plot, our p-values were smaller than expected, indicating the presence of multiple significant associations. Inflation is present, but this expected due to the polygenicity of Major Depression and the the correlation between predicted expression of genes.
MDD TWAS P-value histogram
As shown in the histogram of p-values, our p-values followed a normal distribution as evidenced in the bottom of the graph where a similar amount of p-values is present. Additionally, a peak in correspondence to very small p-values is present at the top of the graph, indicating the presence of signal for our alternative hypothesis.
Create heatmaps for shared and unique associations
# Given the high number of hits we identified (N=177), from 91 unique genes, a single heatmap representing all of such genes cannot be created. Therefore, we depicted our results in two heatmaps: 1) heatmap of genes differentially expressed across multiple SNP-weight sets & 2) heatmap of genes differentially across a single SNP-weight
rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)
#load data
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")
head(twas)
# Extract only certain columns
twas_sub <- twas[,c('ID','PANEL_clean_short','CHR','P0','P1','TWAS.Z', 'TWAS.P')]
str(twas_sub)
str(twas)
#filter for sign. gene IDs only
sign_feat<-twas_sub[twas_sub$TWAS.P < 1.368572e-06,'ID'] #to get a vector with the gene IDs of the significant features
str(sign_feat) #find 176 features as supposed to - genes are repeated though --> you need single gene IDs
sign_genes<-unique(sign_feat) #to identify unique gene IDs
str(sign_genes) #94 gene IDs as expected
twas_sub[order(twas_sub$ID), ]
sign_genes[order(sign_genes$ID), ]
twas_sub <- twas_sub[(twas_sub$ID %in% sign_genes$ID), ]
str(twas_sub) #611 observations where each observation corresponds to an ID in the sign-feat vector
#prepare a vector of gene IDs which are duplicated
duplicates_df<-sign_feat[duplicated(sign_feat)]
duplicates <- duplicates_df$ID
# The following code is necessary to depict results from the CMC DLPFC splicing panel
# Since in RNA-seq splicing multiple transcripts of the same gene are generally tested and we can depict just one in the heatmap, we need to pick the most significant one. This is done below.
###
# RETAIN THE MOST SIGN. FEATURE FROM THE CMC BRAIN SPLICING RESULTS
###
twas_sub <- twas_sub[order(twas_sub$TWAS.P), ] #we order by p-value to make sure that the most significant
#CMC DLPFC feature for a given gene is kept and that its duplicates (which are less sign.) are excluded
head(twas_sub)
tail(twas_sub)
library(dplyr)
twas_sub <- twas_sub %>% distinct(ID, PANEL_clean_short, .keep_all = T) #to get rid of rows which contain duplicates based on the ID and PANEL cols
#in our case, this is just for duplicates in the cmc brain splicing weights with the same gene id
twas_sub_temp <- twas_sub[order(twas_sub$ID), ] #to check that it worked
#there should be just one of the same gene id from brain seq splicing weights
#this is the case, with the mmost sign. one being kept.
#NB 43 gene IDs from the same snp weight were gotten rid of in this df for a total of 568 features instead of 611
####
# Heatmap of genes differentially expressed across multiple SNP-weight sets
####
###
# make a list of features significant in multiple tissues
###
#order files
twas_sub2<-twas_sub[order(twas_sub$ID),]
duplicates<-sort(duplicates, decreasing = FALSE)
#filter the twas_sub datatable by the duplicates vector to obtain a dt with features expressed across diff. tissues only (i.e. no unique features)
twas_sub2<-twas_sub[(twas_sub$ID %in% duplicates), ] #283 obs.
str(twas_sub2)
# Sort the data.frame by CHR and P0
twas_sub2<-twas_sub2[order(twas_sub2$CHR,twas_sub2$P0),]
# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor
twas_sub2$ID<-factor(twas_sub2$ID, levels=unique(twas_sub2$ID))
str(twas_sub2) #there are 36 levels (i.e. 36 unique genes differentially expressed across multiple weights)
#create a vector with all TWAS.Z values
TWAS.Z <- twas_sub2$TWAS.Z
twas_sub2_unique<-twas_sub2[!duplicated(twas_sub2$ID),]
vline_1<-min(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))
vline_2<-max(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))
#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.shared.png",width = 3500, height = 1750, units = "px", res=300)
ggplot(data = twas_sub2, aes(x = ID, y = PANEL_clean_short)) +
#genes as x axis, panel as y axis
theme_bw() + #saying that there will be grid lines
geom_tile(aes(fill = TWAS.Z), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
labs(title="Genes differentially expressed across multiple SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
#title and labels shown for the x and y axes
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83.
####
# Heatmap of genes differentially expressed in one SNP-weight
####
#create a dt with the duplicates removed (!), so that we obtain only unique features
twas_unique<-twas_sub[(!twas_sub$ID %in% duplicates), ]
str(twas_unique) #we get 285 features from differentially expressed genes uniquely differentially expressed in one tissue
# Sort the data.frame by CHR and P0
twas_unique<-twas_unique[order(twas_unique$CHR,twas_unique$P0),] #it's important to order by both
#if you order by pos only, the chr11 stuff will come first!
# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor
twas_unique$ID<-factor(twas_unique$ID, levels=unique(twas_unique$ID))
str(twas_unique)
#create a vector with all TWAS.Z values
TWAS.Z <- twas_unique$TWAS.Z
twas_unique_unique<-twas_unique[!duplicated(twas_unique$ID),]
vline_1<-min(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))
vline_2<-max(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))
#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.unique.png",width = 4650, height = 1750, units = "px", res=300)
ggplot(data = twas_unique, aes(x = ID, y = PANEL_clean_short)) +
theme_bw() +
geom_tile(aes(fill = TWAS.Z), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
labs(title="Genes differentially expressed in single SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83. Create heatmaps for tissue groups
# Heatmaps for groups of tissues were made to show the overlap across SNP-weight panels.
rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)
###
# Load and prepare data
###
#load
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")
#subset columns needed
twas_sub <- twas[,c('ID','PANEL', 'PANEL_clean_short', 'CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P')]
str(twas_sub)
#turn CHR and P0 into numerical variables
twas_sub$CHR <- as.numeric(as.character(twas_sub$CHR))
twas_sub$P0 <- as.numeric(as.character(twas_sub$P0))
str(twas_sub)
###
# Keep only the most sign. feature from the CMC brain splicing results
###
twas_sub2 <- twas_sub[order(twas_sub$TWAS.P), ]
head(twas_sub2)
tail(twas_sub2)
library(dplyr)
twas_sub <- twas_sub2 %>% distinct(ID, PANEL, .keep_all = T)
###
# Create df for groups of tissues
###
#order by CHR and P0 first
twas2 <- twas_sub[order(twas_sub$CHR,twas_sub$P0),]
twas_sub <- twas2
#create df
twas_brain.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Amygdala", "Brain_Anterior_cingulate_cortex_BA24", "Brain_Caudate_basal_ganglia", "Brain_Cerebellar_Hemisphere", "Brain_Cerebellum", "Brain_Cortex", "Brain_Frontal_Cortex_BA9", "Brain_Hippocampus", "Brain_Hypothalamus", "Brain_Nucleus_accumbens_basal_ganglia", "Brain_Putamen_basal_ganglia", "Brain_Substantia_nigra", "CMC.BRAIN.RNASEQ", "CMC.BRAIN.RNASEQ_SPLICING","PsychENCODE"), ]
twas_blood.df <- twas_sub[twas_sub$PANEL %in% c("Whole_Blood", "NTR.BLOOD.RNAARR", "YFS.BLOOD.RNAARR"), ]
twas_HPA.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary", "Adrenal_Gland"), ]
twas_HPT.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary","Thyroid"), ]
###
#filter for gene IDs significant in a given group of tissues only
###
#get sign. gene ID per group of tissues
sign_feat_brain<-twas_brain.df[twas_brain.df$TWAS.P < 1.368572e-06,]$ID #to get a vector with the gene IDs of the significant features
#101 features are sign. within brain snp weight sets
str(sign_feat_brain)
sign_feat_brain <- unique(sign_feat_brain) #111 unique genes differentially expressed in brain snp weights
sign_feat_blood<-twas_blood.df[twas_blood.df$TWAS.P < 1.368572e-06,]$ID
str(sign_feat_blood) #26
sign_feat_blood <- unique(sign_feat_blood)#23
sign_feat_HPA<-twas_HPA.df[twas_HPA.df$TWAS.P < 1.368572e-06,]$ID #to get a vector with the gene IDs of the significant features
str(sign_feat_HPA) #28
sign_feat_HPA <- unique(sign_feat_HPA) #22
sign_feat_HPT<-twas_HPT.df[twas_HPT.df$TWAS.P < 1.368572e-06,]$ID #to get a vector with the gene IDs of the significant features
str(sign_feat_HPT) #41
sign_feat_HPT <- unique(sign_feat_HPT) #34
#filter
twas_brain.df <- twas_brain.df[(twas_brain.df$ID %in% sign_feat_brain), ]
#320 obs.
twas_blood.df <- twas_blood.df[(twas_blood.df$ID %in% sign_feat_blood), ] #32
twas_HPA.df <- twas_HPA.df[(twas_HPA.df$ID %in% sign_feat_HPA), ] #32
twas_HPT.df <- twas_HPT.df[(twas_HPT.df$ID %in% sign_feat_HPT), ] #51 features with the gene ID within the vector sign feat...
#create vectors with the z scores of the features within specific tissues
TWAS.Z.brain <- twas_brain.df$TWAS.Z
TWAS.Z.blood <- twas_blood.df$TWAS.Z
TWAS.Z.HPA <- twas_HPA.df$TWAS.Z
TWAS.Z.HPT <- twas_HPT.df$TWAS.Z
# Make ID a factor for plotting
twas_brain.df$ID<-factor(twas_brain.df$ID, levels = unique(twas_brain.df$ID))
twas_blood.df$ID<-factor(twas_blood.df$ID, levels = unique(twas_blood.df$ID))
twas_HPA.df$ID<-factor(twas_HPA.df$ID, levels = unique(twas_HPA.df$ID))
twas_HPT.df$ID<-factor(twas_HPT.df$ID, levels = unique(twas_HPT.df$ID))
#####
# Heatmap for brain SNP-weight sets
#####
twas_brain.df_unique<-twas_brain.df[!duplicated(twas_brain.df$ID),]
vline_1<-min(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))
vline_2<-max(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))
#Plot brain SNP weights
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_brain.png",width = 5100, height = 1400, units = "px", res=300)
ggplot(data = twas_brain.df, aes(x = ID, y = PANEL_clean_short)) +
theme_bw() +
geom_tile(aes(fill = TWAS.Z.brain), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z.brain,1)), color="black", size=3) +
labs(title="Genes differentially expressed in brain SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83.
#####
# Plot for blood SNP-weight sets
#####
twas_blood.df_unique<-twas_blood.df[!duplicated(twas_blood.df$ID),]
vline_1<-min(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))
vline_2<-max(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))
#Plot blood findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_blood.png",width = 2400, height = 800, units = "px", res=300)
ggplot(data = twas_blood.df, aes(x = ID, y = PANEL_clean_short)) +
theme_bw() +
geom_tile(aes(fill = TWAS.Z.blood), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z.blood,1)), color="black", size=3) +
labs(title="Genes differentially expressed in blood SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83.
#####
# Plot for HPA axis SNP-weight sets
#####
twas_HPA.df_unique<-twas_HPA.df[!duplicated(twas_HPA.df$ID),]
vline_1<-min(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))
#Plot HPA axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPA.png",width = 2400, height = 800, units = "px", res=300)
ggplot(data = twas_HPA.df, aes(x = ID, y = PANEL_clean_short)) +
theme_bw() +
geom_tile(aes(fill = TWAS.Z.HPA), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z.HPA,1)), color="black", size=3) +
labs(title="Genes differentially expressed in HPA axis SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83.
#####
# Plot for HPT axis SNP-weight sets
#####
twas_HPT.df_unique<-twas_HPT.df[!duplicated(twas_HPT.df$ID),]
vline_1<-min(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))
#Plot HPT axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPT.png",width = 3000, height = 800, units = "px", res=300)
ggplot(data = twas_HPT.df, aes(x = ID, y = PANEL_clean_short)) +
theme_bw() +
geom_tile(aes(fill = TWAS.Z.HPT), colour = 'black') +
scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
geom_text(aes(label=round(TWAS.Z.HPT,1)), color="black", size=3) +
labs(title="Genes differentially expressed in HPT axis SNP-weight sets", x ="Gene ID", y = "SNP-weight sets") +
geom_vline(xintercept = vline_1-0.5, size=1) +
geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()
# Note significant features presented a z-score > 4.83 or < -4.83. Show plots
MDD TWAS Heatmap Shared
MDD TWAS Heatmap Unique
***
MDD TWAS Heatmap Blood
MDD TWAS Heatmap HPA
MDD TWAS Heatmap HPT
3.5 Process colocalisation results
Colocalisation identified whether TWAS and GWAS associations result from the same or distinct causal SNP.
Organise coloc results
###
# Create a table with colocalisation results for all significant features
###
rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
library(tibble)
twas_sign <- as_data_frame(twas_sign)
colnames(twas_sign)
twas_sign$Location<-paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1)
#transform variables into numeric and then order by them
str(twas_sign)
as.numeric(as.character(twas_sign$CHR))
as.numeric(as.character(twas_sign$P0))
twas_sign_ordered <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]
col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4")
twas_sign_ordered <- twas_sign_ordered[, col_order]
twas_sign_ordered
###
#Create a couple of additional columns specifying whether the feature is colocalised or not
###
#to specify coloc pp4 > 0.8 (see gusev et al (2019) Nat Genet on epithelial ovarian cancer)
twas_sign_ordered$High_PP4_0.8 <- NULL
twas_sign_ordered$High_PP4_0.8 <- ifelse(twas_sign_ordered$COLOC.PP4 > 0.8, "Yes", "No")
sum(twas_sign_ordered$High_PP4_0.8 == "Yes") #97 features present a PP4 greater than 0.8
#to specify coloc pp3 < 0.2
twas_sign_ordered$Low_PP3_0.2 <- NULL
twas_sign_ordered$Low_PP3_0.2 <- ifelse(twas_sign_ordered$COLOC.PP3 < 0.2, "Yes", "No")
sum(twas_sign_ordered$Low_PP3_0.2 == "Yes") #140 features present a PP3 smaller than 0.2
#specify whether both conditions are satisfied (NB PP4 > 0.8 is much more of a stringent threshold)
twas_sign_ordered$Colocalised <- NULL
twas_sign_ordered$Colocalised <- ifelse(twas_sign_ordered$High_PP4_0.8 == "Yes" & twas_sign_ordered$Low_PP3_0.2 == "Yes", "Yes", "No")
sum(twas_sign_ordered$Colocalised == "Yes") #97 features are colocalised
#get the number of unique genes which were colocalised
colocalised_df <- twas_sign_ordered[twas_sign_ordered$Colocalised == "Yes", ] #as expected, dim = 97, 13
colocalised_vector <- colocalised_df$ID
unique_genes_colocalised <- unique(colocalised_vector) #57 unique genes which were colocalised
###
# Clean and Save
###
col_order2 <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4", "Low_PP3_0.2", "High_PP4_0.8", "Colocalised")
twas_sign_ordered <- twas_sign_ordered[, col_order2]
twas_sign_ordered
write.csv(twas_sign_ordered, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_colocalisation.csv", row.names = F)
q()
nShow colocalisation table
| Location | ID | PANEL_clean_short | TWAS.Z | TWAS.P | COLOC.PP0 | COLOC.PP1 | COLOC.PP2 | COLOC.PP3 | COLOC.PP4 | Low_PP3_0.2 | High_PP4_0.8 | Colocalised |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| chr1:8412457-8877702 | RERE | GTEx Whole Blood | -5.095707 | 3.47e-07 | 0.000 | 0.001 | 0.000 | 0.006 | 0.993 | Yes | Yes | Yes |
| chr1:8412457-8877702 | RERE | YFS Blood | -5.310078 | 1.10e-07 | 0.000 | 0.001 | 0.000 | 0.006 | 0.993 | Yes | Yes | Yes |
| chr1:8484705-8494898 | RP5-1115A15.1 | GTEx Thyroid | -5.175240 | 2.28e-07 | 0.000 | 0.001 | 0.000 | 0.004 | 0.995 | Yes | Yes | Yes |
| chr1:8484705-8494898 | RP5-1115A15.1 | GTEx Whole Blood | -4.866386 | 1.14e-06 | 0.012 | 0.001 | 0.088 | 0.008 | 0.891 | Yes | Yes | Yes |
| chr1:36884051-36884179 | SNORA63 | GTEx Nucleus accumbens | 4.848870 | 1.24e-06 | 0.067 | 0.006 | 0.257 | 0.023 | 0.647 | Yes | No | No |
| chr1:71861623-72748417 | NEGR1 | GTEx Caudate | 5.780100 | 7.47e-09 | 0.000 | 0.000 | 0.284 | 0.036 | 0.681 | Yes | No | No |
| chr1:71861623-72748417 | NEGR1 | GTEx Putamen | 5.548510 | 2.88e-08 | 0.000 | 0.000 | 0.018 | 0.014 | 0.968 | Yes | Yes | Yes |
| chr1:71861623-72748417 | NEGR1 | GTEx Whole Blood | 8.760622 | 1.94e-18 | 0.000 | 0.000 | 0.000 | 0.007 | 0.993 | Yes | Yes | Yes |
| chr1:72767155-72767512 | RPL31P12 | GTEx Cerebellar Hemispher… | -7.785520 | 6.94e-15 | 0.000 | 0.000 | 0.000 | 0.010 | 0.990 | Yes | Yes | Yes |
| chr1:72767155-72767512 | RPL31P12 | GTEx Cerebellum | -7.708820 | 1.27e-14 | 0.000 | 0.000 | 0.000 | 0.006 | 0.994 | Yes | Yes | Yes |
| chr1:72767155-72767512 | RPL31P12 | PsychENCODE | -7.742756 | 9.73e-15 | 0.000 | 0.000 | 0.000 | 0.007 | 0.993 | Yes | Yes | Yes |
| chr1:175873898-175889649 | RP11-318C24.2 | GTEx Thyroid | -5.027510 | 4.97e-07 | 0.004 | 0.007 | 0.012 | 0.018 | 0.959 | Yes | Yes | Yes |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | -4.958690 | 7.10e-07 | 0.000 | 0.008 | 0.000 | 0.021 | 0.971 | Yes | Yes | Yes |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | 5.039850 | 4.66e-07 | 0.000 | 0.007 | 0.000 | 0.020 | 0.973 | Yes | Yes | Yes |
| chr1:175913966-176176370 | RFWD2 | CMC DLPFC Splicing | -5.005960 | 5.56e-07 | 0.000 | 0.007 | 0.000 | 0.019 | 0.973 | Yes | Yes | Yes |
| chr1:181452685-181775921 | CACNA1E | CMC DLPFC Splicing | -4.989390 | 6.06e-07 | 0.000 | 0.151 | 0.000 | 0.420 | 0.429 | No | No | No |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC | 4.848374 | 1.24e-06 | 0.000 | 0.001 | 0.000 | 0.011 | 0.988 | Yes | Yes | Yes |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC Splicing | -5.421950 | 5.90e-08 | 0.000 | 0.001 | 0.000 | 0.010 | 0.989 | Yes | Yes | Yes |
| chr1:197473878-197744623 | DENND1B | CMC DLPFC Splicing | 5.018050 | 5.22e-07 | 0.000 | 0.001 | 0.000 | 0.010 | 0.989 | Yes | Yes | Yes |
| chr2:58386377-58468515 | FANCL | CMC DLPFC | -5.183180 | 2.18e-07 | 0.001 | 0.000 | 0.056 | 0.027 | 0.916 | Yes | Yes | Yes |
| chr2:58386377-58468515 | FANCL | CMC DLPFC Splicing | 4.897476 | 9.71e-07 | 0.000 | 0.001 | 0.007 | 0.104 | 0.888 | Yes | Yes | Yes |
| chr2:197831741-198175897 | ANKRD44 | YFS Blood | -5.690140 | 1.27e-08 | 0.062 | 0.023 | 0.168 | 0.061 | 0.686 | Yes | No | No |
| chr2:198254508-198299815 | SF3B1 | GTEx Hypothalamus | 5.214900 | 1.84e-07 | 0.071 | 0.015 | 0.319 | 0.068 | 0.526 | Yes | No | No |
| chr3:44481261-44561226 | ZNF445 | CMC DLPFC | -5.103280 | 3.34e-07 | 0.000 | 0.087 | 0.002 | 0.601 | 0.310 | No | No | No |
| chr4:41937137-41962589 | TMEM33 | PsychENCODE | 4.837418 | 1.32e-06 | 0.000 | 0.001 | 0.000 | 0.074 | 0.925 | Yes | Yes | Yes |
| chr4:41983713-41988476 | DCAF4L1 | GTEx Thyroid | -5.128000 | 2.93e-07 | 0.003 | 0.001 | 0.291 | 0.091 | 0.615 | Yes | No | No |
| chr4:41990758-41991254 | RP11-814H16.2 | GTEx Cerebellar Hemispher… | 5.009600 | 5.45e-07 | 0.005 | 0.000 | 0.561 | 0.055 | 0.378 | Yes | No | No |
| chr4:41992489-42092474 | SLC30A9 | GTEx Amygdala | -5.253400 | 1.49e-07 | 0.004 | 0.001 | 0.476 | 0.070 | 0.450 | Yes | No | No |
| chr4:41992489-42092474 | SLC30A9 | GTEx ACC | -5.001690 | 5.68e-07 | 0.003 | 0.001 | 0.399 | 0.101 | 0.496 | Yes | No | No |
| chr4:41992489-42092474 | SLC30A9 | GTEx Caudate | -4.854800 | 1.21e-06 | 0.003 | 0.001 | 0.388 | 0.086 | 0.521 | Yes | No | No |
| chr4:41992489-42092474 | SLC30A9 | GTEx Cortex | -5.774530 | 7.72e-09 | 0.003 | 0.001 | 0.340 | 0.113 | 0.543 | Yes | No | No |
| chr4:41992489-42092474 | SLC30A9 | GTEx Hypothalamus | -5.085140 | 3.67e-07 | 0.000 | 0.000 | 0.016 | 0.039 | 0.944 | Yes | Yes | Yes |
| chr4:41992489-42092474 | SLC30A9 | GTEx Nucleus accumbens | -5.602700 | 2.11e-08 | 0.001 | 0.001 | 0.072 | 0.119 | 0.808 | Yes | Yes | Yes |
| chr4:41992489-42092474 | SLC30A9 | PsychENCODE | -5.259200 | 1.45e-07 | 0.000 | 0.000 | 0.000 | 0.026 | 0.974 | Yes | Yes | Yes |
| chr5:87564712-87732502 | TMEM161B-AS1 | PsychENCODE | 6.091010 | 1.12e-09 | 0.000 | 0.000 | 0.000 | 0.117 | 0.883 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Adrenal Gland | 5.360090 | 8.32e-08 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Amygdala | 6.118500 | 9.45e-10 | 0.000 | 0.000 | 0.004 | 0.058 | 0.938 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx ACC | 6.445500 | 1.15e-10 | 0.000 | 0.000 | 0.000 | 0.056 | 0.944 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Caudate | 6.282167 | 3.34e-10 | 0.000 | 0.000 | 0.000 | 0.062 | 0.938 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cerebellar Hemispher… | 6.011700 | 1.84e-09 | 0.000 | 0.000 | 0.000 | 0.062 | 0.938 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cerebellum | 6.053050 | 1.42e-09 | 0.000 | 0.000 | 0.000 | 0.052 | 0.948 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Cortex | 6.021420 | 1.73e-09 | 0.000 | 0.000 | 0.000 | 0.070 | 0.930 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Frontal Cortex | 6.720000 | 1.82e-11 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Hypothalamus | 5.875800 | 4.21e-09 | 0.000 | 0.000 | 0.000 | 0.060 | 0.940 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Nucleus accumbens | 6.010490 | 1.85e-09 | 0.000 | 0.000 | 0.000 | 0.059 | 0.941 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Putamen | 6.372050 | 1.87e-10 | 0.000 | 0.000 | 0.000 | 0.054 | 0.946 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Substantia nigra | 6.057270 | 1.38e-09 | 0.000 | 0.000 | 0.009 | 0.054 | 0.937 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Pituitary | 6.048500 | 1.46e-09 | 0.000 | 0.000 | 0.000 | 0.050 | 0.950 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Thyroid | 5.889760 | 3.87e-09 | 0.000 | 0.000 | 0.000 | 0.079 | 0.920 | Yes | Yes | Yes |
| chr5:87564888-87732502 | TMEM161B-AS1 | GTEx Whole Blood | 5.526440 | 3.27e-08 | 0.000 | 0.000 | 0.000 | 0.048 | 0.952 | Yes | Yes | Yes |
| chr5:87729709-87794514 | CTC-498M16.4 | GTEx Substantia nigra | 5.403610 | 6.53e-08 | 0.000 | 0.000 | 0.391 | 0.037 | 0.572 | Yes | No | No |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx ACC | -5.813700 | 6.11e-09 | 0.000 | 0.000 | 0.631 | 0.053 | 0.317 | Yes | No | No |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Cerebellar Hemispher… | -5.861000 | 4.60e-09 | 0.000 | 0.000 | 0.056 | 0.051 | 0.893 | Yes | Yes | Yes |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Cortex | -6.510990 | 7.47e-11 | 0.000 | 0.000 | 0.091 | 0.040 | 0.870 | Yes | Yes | Yes |
| chr5:87988462-87989789 | CTC-467M3.3 | GTEx Frontal Cortex | -7.091600 | 1.33e-12 | 0.000 | 0.000 | 0.114 | 0.035 | 0.850 | Yes | Yes | Yes |
| chr5:87988462-87989789 | CTC-467M3.3 | PsychENCODE | -6.097890 | 1.07e-09 | 0.000 | 0.000 | 0.035 | 0.251 | 0.715 | No | No | No |
| chr5:140024947-140027370 | NDUFA2 | CMC DLPFC | 5.190020 | 2.10e-07 | 0.104 | 0.037 | 0.170 | 0.060 | 0.629 | Yes | No | No |
| chr5:140201222-140203811 | PCDHA5 | GTEx Thyroid | -5.402970 | 6.55e-08 | 0.055 | 0.028 | 0.090 | 0.046 | 0.781 | Yes | No | No |
| chr5:140220907-140223351 | PCDHA8 | GTEx Cerebellar Hemispher… | -4.980100 | 6.36e-07 | 0.001 | 0.032 | 0.001 | 0.051 | 0.915 | Yes | Yes | Yes |
| chr6:26188921-26189323 | HIST1H4D | NTR Blood | -4.987600 | 6.11e-07 | 0.000 | 0.000 | 0.309 | 0.059 | 0.632 | Yes | No | No |
| chr6:26365386-26378540 | BTN3A2 | NTR Blood | 5.326600 | 1.00e-07 | 0.000 | 0.000 | 0.000 | 0.562 | 0.438 | No | No | No |
| chr6:26365387-26378546 | BTN3A2 | GTEx Cerebellar Hemispher… | 5.188200 | 2.12e-07 | 0.000 | 0.000 | 0.000 | 0.505 | 0.495 | No | No | No |
| chr6:26365387-26378546 | BTN3A2 | GTEx Hippocampus | 4.963000 | 6.96e-07 | 0.000 | 0.000 | 0.000 | 0.506 | 0.494 | No | No | No |
| chr6:26365387-26378546 | BTN3A2 | GTEx Pituitary | 5.898930 | 3.66e-09 | 0.000 | 0.000 | 0.000 | 0.501 | 0.499 | No | No | No |
| chr6:26365387-26378546 | BTN3A2 | GTEx Thyroid | 5.481600 | 4.22e-08 | 0.000 | 0.000 | 0.000 | 0.507 | 0.492 | No | No | No |
| chr6:26365387-26378546 | BTN3A2 | GTEx Whole Blood | 5.086960 | 3.64e-07 | 0.000 | 0.000 | 0.000 | 0.543 | 0.457 | No | No | No |
| chr6:26538633-26546482 | HMGN4 | GTEx Cerebellum | 5.395400 | 6.84e-08 | 0.000 | 0.000 | 0.404 | 0.299 | 0.297 | No | No | No |
| chr6:27215480-27224250 | PRSS16 | GTEx Cerebellar Hemispher… | -4.891200 | 1.00e-06 | 0.000 | 0.000 | 0.096 | 0.322 | 0.582 | No | No | No |
| chr6:27215480-27224250 | PRSS16 | GTEx Cerebellum | -4.947900 | 7.50e-07 | 0.000 | 0.001 | 0.000 | 0.962 | 0.037 | No | No | No |
| chr6:27215480-27224250 | PRSS16 | GTEx Frontal Cortex | -5.045000 | 4.54e-07 | 0.000 | 0.000 | 0.366 | 0.056 | 0.577 | Yes | No | No |
| chr6:27215480-27224250 | PRSS16 | GTEx Pituitary | -5.916080 | 3.30e-09 | 0.000 | 0.000 | 0.044 | 0.035 | 0.921 | Yes | Yes | Yes |
| chr6:27215480-27224250 | PRSS16 | GTEx Whole Blood | -5.335920 | 9.51e-08 | 0.000 | 0.001 | 0.047 | 0.474 | 0.479 | No | No | No |
| chr6:27325604-27339304 | ZNF204P | GTEx Adrenal Gland | -5.032700 | 4.84e-07 | 0.000 | 0.000 | 0.547 | 0.211 | 0.242 | No | No | No |
| chr6:27371789-27374743 | RP1-153G14.4 | GTEx Hippocampus | 5.354000 | 8.60e-08 | 0.000 | 0.000 | 0.676 | 0.142 | 0.182 | Yes | No | No |
| chr6:27418522-27440897 | ZNF184 | GTEx Caudate | -6.325200 | 2.53e-10 | 0.000 | 0.000 | 0.231 | 0.114 | 0.655 | Yes | No | No |
| chr6:27418522-27440897 | ZNF184 | GTEx Hypothalamus | -4.952200 | 7.34e-07 | 0.000 | 0.000 | 0.174 | 0.185 | 0.641 | Yes | No | No |
| chr6:27840926-27841289 | HIST1H4L | NTR Blood | 4.870800 | 1.11e-06 | 0.000 | 0.000 | 0.001 | 0.162 | 0.838 | Yes | Yes | Yes |
| chr6:28058932-28061442 | ZSCAN12P1 | PsychENCODE | 6.268010 | 3.66e-10 | 0.000 | 0.000 | 0.056 | 0.626 | 0.318 | No | No | No |
| chr6:28058932-28061442 | ZSCAN12P1 | GTEx Whole Blood | -4.936930 | 7.94e-07 | 0.000 | 0.000 | 0.388 | 0.365 | 0.247 | No | No | No |
| chr6:28083406-28084329 | RP1-265C24.5 | GTEx Hippocampus | 5.532000 | 3.16e-08 | 0.000 | 0.000 | 0.009 | 0.143 | 0.847 | Yes | Yes | Yes |
| chr6:28092338-28097860 | ZSCAN16 | YFS Blood | -6.109000 | 1.00e-09 | 0.000 | 0.000 | 0.013 | 0.054 | 0.933 | Yes | Yes | Yes |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Cerebellum | -5.307800 | 1.11e-07 | 0.000 | 0.000 | 0.238 | 0.147 | 0.615 | Yes | No | No |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Hippocampus | -6.017000 | 1.77e-09 | 0.000 | 0.000 | 0.080 | 0.053 | 0.866 | Yes | Yes | Yes |
| chr6:28192664-28201260 | ZSCAN9 | GTEx Pituitary | -6.159020 | 7.32e-10 | 0.000 | 0.000 | 0.169 | 0.191 | 0.640 | Yes | No | No |
| chr6:28227098-28228736 | NKAPL | PsychENCODE | 5.002860 | 5.65e-07 | 0.000 | 0.000 | 0.627 | 0.332 | 0.041 | No | No | No |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Adrenal Gland | 5.094600 | 3.49e-07 | 0.000 | 0.000 | 0.031 | 0.146 | 0.822 | Yes | Yes | Yes |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Cerebellum | 5.062800 | 4.13e-07 | 0.000 | 0.000 | 0.000 | 0.756 | 0.244 | No | No | No |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Hippocampus | 5.198000 | 2.01e-07 | 0.000 | 0.000 | 0.172 | 0.174 | 0.654 | Yes | No | No |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Putamen | 5.739000 | 9.52e-09 | 0.000 | 0.000 | 0.018 | 0.051 | 0.931 | Yes | Yes | Yes |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Thyroid | 5.338400 | 9.38e-08 | 0.000 | 0.000 | 0.000 | 0.858 | 0.141 | No | No | No |
| chr6:28234788-28245974 | RP5-874C20.3 | GTEx Whole Blood | 5.662330 | 1.49e-08 | 0.000 | 0.000 | 0.288 | 0.107 | 0.604 | Yes | No | No |
| chr6:28249314-28270326 | PGBD1 | GTEx Cerebellar Hemispher… | -6.313100 | 2.74e-10 | 0.000 | 0.000 | 0.032 | 0.017 | 0.950 | Yes | Yes | Yes |
| chr6:28292470-28324048 | ZSCAN31 | GTEx Amygdala | -5.084150 | 3.69e-07 | 0.000 | 0.000 | 0.381 | 0.404 | 0.214 | No | No | No |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Amygdala | 4.949900 | 7.43e-07 | 0.000 | 0.000 | 0.777 | 0.111 | 0.111 | Yes | No | No |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Hippocampus | 4.951000 | 7.37e-07 | 0.000 | 0.000 | 0.389 | 0.273 | 0.338 | No | No | No |
| chr6:28317691-28336947 | ZKSCAN3 | GTEx Thyroid | 6.093300 | 1.11e-09 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | Yes | Yes | Yes |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Hypothalamus | -5.777500 | 7.58e-09 | 0.000 | 0.000 | 0.062 | 0.179 | 0.758 | Yes | No | No |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Putamen | -4.894000 | 9.90e-07 | 0.000 | 0.000 | 0.239 | 0.226 | 0.535 | No | No | No |
| chr6:28399707-28411279 | ZSCAN23 | GTEx Pituitary | -4.953290 | 7.30e-07 | 0.000 | 0.000 | 0.002 | 0.275 | 0.723 | No | No | No |
| chr6:30644166-30655672 | PPP1R18 | GTEx Adrenal Gland | 4.910200 | 9.10e-07 | 0.106 | 0.007 | 0.130 | 0.007 | 0.750 | Yes | No | No |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | -5.299700 | 1.16e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | Yes | Yes | Yes |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | -5.067100 | 4.04e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | Yes | Yes | Yes |
| chr6:30695485-30710682 | FLOT1 | CMC DLPFC Splicing | 4.936600 | 7.95e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | Yes | Yes | Yes |
| chr6:30695486-30710510 | FLOT1 | GTEx Cerebellum | -5.299000 | 1.16e-07 | 0.010 | 0.001 | 0.012 | 0.000 | 0.976 | Yes | Yes | Yes |
| chr6:30695486-30710510 | FLOT1 | GTEx Pituitary | -5.253270 | 1.49e-07 | 0.016 | 0.001 | 0.020 | 0.000 | 0.963 | Yes | Yes | Yes |
| chr6:30695486-30710510 | FLOT1 | GTEx Thyroid | -5.557400 | 2.74e-08 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | Yes | Yes | Yes |
| chr6:30881982-30894236 | VARS2 | GTEx Cortex | 5.922000 | 3.18e-09 | 0.105 | 0.004 | 0.158 | 0.005 | 0.727 | Yes | No | No |
| chr6:30881982-30894236 | VARS2 | GTEx Whole Blood | 6.323130 | 2.56e-10 | 0.005 | 0.001 | 0.007 | 0.000 | 0.986 | Yes | Yes | Yes |
| chr6:31255287-31256741 | WASF5P | GTEx Pituitary | -5.156240 | 2.52e-07 | 0.000 | 0.046 | 0.000 | 0.091 | 0.862 | Yes | Yes | Yes |
| chr6:31368479-31445283 | HCP5 | GTEx Thyroid | 6.400800 | 1.55e-10 | 0.000 | 0.008 | 0.000 | 0.015 | 0.976 | Yes | Yes | Yes |
| chr6:31462658-31478901 | MICB | GTEx Thyroid | -5.557000 | 2.74e-08 | 0.000 | 0.047 | 0.000 | 0.094 | 0.859 | Yes | Yes | Yes |
| chr6:31606805-31620482 | BAG6 | CMC DLPFC Splicing | -5.580000 | 2.40e-08 | 0.008 | 0.408 | 0.006 | 0.319 | 0.259 | No | No | No |
| chr6:31694815-31698357 | DDAH2 | GTEx Frontal Cortex | 5.409500 | 6.32e-08 | 0.331 | 0.042 | 0.258 | 0.033 | 0.336 | Yes | No | No |
| chr6:31694816-31698039 | DDAH2 | CMC DLPFC | 5.344500 | 9.07e-08 | 0.000 | 0.051 | 0.000 | 0.039 | 0.909 | Yes | Yes | Yes |
| chr6:99817347-99842082 | COQ3 | CMC DLPFC Splicing | 5.146560 | 2.65e-07 | 0.324 | 0.015 | 0.598 | 0.029 | 0.034 | Yes | No | No |
| chr6:105404922-105531207 | LIN28B | CMC DLPFC | -5.232050 | 1.68e-07 | 0.000 | 0.001 | 0.000 | 0.008 | 0.990 | Yes | Yes | Yes |
| chr6:105404923-105531207 | LIN28B | PsychENCODE | -5.105689 | 3.30e-07 | 0.000 | 0.005 | 0.000 | 0.052 | 0.943 | Yes | Yes | Yes |
| chr6:105584224-105617820 | BVES-AS1 | GTEx Amygdala | -5.578300 | 2.43e-08 | 0.061 | 0.007 | 0.373 | 0.045 | 0.514 | Yes | No | No |
| chr7:12250867-12282993 | TMEM106B | GTEx Adrenal Gland | 5.505026 | 3.69e-08 | 0.000 | 0.001 | 0.003 | 0.009 | 0.987 | Yes | Yes | Yes |
| chr7:12250867-12282993 | TMEM106B | PsychENCODE | -5.790690 | 7.01e-09 | 0.000 | 0.001 | 0.000 | 0.054 | 0.945 | Yes | Yes | Yes |
| chr7:12250867-12282993 | TMEM106B | GTEx Whole Blood | 5.531000 | 3.18e-08 | 0.000 | 0.001 | 0.000 | 0.008 | 0.991 | Yes | Yes | Yes |
| chr7:12250867-12276886 | TMEM106B | YFS Blood | 5.373600 | 7.72e-08 | 0.000 | 0.001 | 0.000 | 0.007 | 0.993 | Yes | Yes | Yes |
| chr7:24836158-25021253 | OSBPL3 | GTEx Pituitary | -5.622890 | 1.88e-08 | 0.090 | 0.040 | 0.062 | 0.027 | 0.780 | Yes | No | No |
| chr8:52232136-52722005 | PXDNL | CMC DLPFC | 5.887460 | 3.92e-09 | 0.090 | 0.019 | 0.318 | 0.065 | 0.508 | Yes | No | No |
| chr8:61297147-61429354 | RP11-163N6.2 | GTEx Thyroid | -5.336530 | 9.47e-08 | 0.084 | 0.162 | 0.118 | 0.228 | 0.408 | No | No | No |
| chr9:126605315-126605965 | PIGFP2 | PsychENCODE | -5.305600 | 1.12e-07 | 0.017 | 0.004 | 0.603 | 0.126 | 0.250 | Yes | No | No |
| chr11:57067112-57092426 | TNKS1BP1 | GTEx Adrenal Gland | 4.922610 | 8.54e-07 | 0.080 | 0.025 | 0.107 | 0.032 | 0.756 | Yes | No | No |
| chr11:57405497-57420263 | AP000662.4 | GTEx Thyroid | -4.980256 | 6.35e-07 | 0.000 | 0.136 | 0.000 | 0.255 | 0.610 | No | No | No |
| chr11:57424488-57429340 | CLP1 | GTEx Whole Blood | 5.195860 | 2.04e-07 | 0.001 | 0.008 | 0.002 | 0.015 | 0.974 | Yes | Yes | Yes |
| chr11:61535973-61560274 | TMEM258 | PsychENCODE | 5.021730 | 5.12e-07 | 0.000 | 0.049 | 0.000 | 0.041 | 0.910 | Yes | Yes | Yes |
| chr11:113280318-113346111 | DRD2 | GTEx Frontal Cortex | -5.073787 | 3.90e-07 | 0.366 | 0.032 | 0.515 | 0.045 | 0.042 | Yes | No | No |
| chr13:53602875-53626196 | OLFM4 | CMC DLPFC | 5.091290 | 3.56e-07 | 0.000 | 0.000 | 0.865 | 0.089 | 0.046 | Yes | No | No |
| chr14:42057064-42074059 | CTD-2298J14.2 | GTEx Thyroid | -5.678860 | 1.36e-08 | 0.000 | 0.000 | 0.000 | 0.022 | 0.978 | Yes | Yes | Yes |
| chr14:42076773-42373752 | LRFN5 | GTEx Cerebellar Hemispher… | 5.423400 | 5.85e-08 | 0.000 | 0.000 | 0.000 | 0.029 | 0.971 | Yes | Yes | Yes |
| chr14:42076773-42373752 | LRFN5 | GTEx Cerebellum | 5.597540 | 2.17e-08 | 0.000 | 0.000 | 0.000 | 0.041 | 0.959 | Yes | Yes | Yes |
| chr14:59951161-59971429 | JKAMP | GTEx Thyroid | -5.125100 | 2.97e-07 | 0.001 | 0.004 | 0.004 | 0.022 | 0.969 | Yes | Yes | Yes |
| chr14:59971779-60043549 | CCDC175 | GTEx Thyroid | -5.478850 | 4.28e-08 | 0.000 | 0.004 | 0.000 | 0.018 | 0.979 | Yes | Yes | Yes |
| chr14:60062693-60337557 | RTN1 | CMC DLPFC Splicing | -4.874920 | 1.09e-06 | 0.001 | 0.006 | 0.007 | 0.033 | 0.953 | Yes | Yes | Yes |
| chr14:60062695-60337684 | RTN1 | GTEx Thyroid | -5.348450 | 8.87e-08 | 0.000 | 0.003 | 0.000 | 0.016 | 0.981 | Yes | Yes | Yes |
| chr14:64319682-64693151 | SYNE2 | NTR Blood | 5.609528 | 2.03e-08 | 0.000 | 0.000 | 0.000 | 0.016 | 0.984 | Yes | Yes | Yes |
| chr14:64550950-64770377 | ESR2 | GTEx Pituitary | -5.982300 | 2.20e-09 | 0.000 | 0.000 | 0.113 | 0.026 | 0.860 | Yes | Yes | Yes |
| chr14:64550950-64770377 | ESR2 | GTEx Whole Blood | -5.655371 | 1.56e-08 | 0.000 | 0.000 | 0.000 | 0.014 | 0.986 | Yes | Yes | Yes |
| chr14:75120140-75179818 | AREL1 | PsychENCODE | -5.015110 | 5.30e-07 | 0.000 | 0.002 | 0.000 | 0.216 | 0.782 | No | No | No |
| chr14:75319736-75330537 | PROX2 | GTEx Thyroid | -5.758100 | 8.51e-09 | 0.000 | 0.000 | 0.017 | 0.020 | 0.962 | Yes | Yes | Yes |
| chr14:75348593-75370450 | DLST | CMC DLPFC | 4.981400 | 6.31e-07 | 0.000 | 0.001 | 0.000 | 0.047 | 0.952 | Yes | Yes | Yes |
| chr14:75348594-75370448 | DLST | PsychENCODE | 5.089700 | 3.59e-07 | 0.000 | 0.000 | 0.000 | 0.023 | 0.977 | Yes | Yes | Yes |
| chr14:75370656-75389188 | RPS6KL1 | CMC DLPFC Splicing | -5.023810 | 5.07e-07 | 0.003 | 0.001 | 0.205 | 0.082 | 0.708 | Yes | No | No |
| chr14:75370657-75390099 | RPS6KL1 | PsychENCODE | -4.952550 | 7.32e-07 | 0.002 | 0.000 | 0.176 | 0.031 | 0.791 | Yes | No | No |
| chr14:103878456-103879098 | RP11-600F24.2 | PsychENCODE | 5.185660 | 2.15e-07 | 0.007 | 0.002 | 0.552 | 0.202 | 0.238 | No | No | No |
| chr14:103985996-103989448 | CKB | YFS Blood | 5.346000 | 8.99e-08 | 0.000 | 0.001 | 0.000 | 0.005 | 0.995 | Yes | Yes | Yes |
| chr14:103995508-104003410 | TRMT61A | CMC DLPFC | 5.051300 | 4.39e-07 | 0.001 | 0.004 | 0.004 | 0.030 | 0.961 | Yes | Yes | Yes |
| chr14:103995521-104003410 | TRMT61A | GTEx Whole Blood | 4.977593 | 6.44e-07 | 0.006 | 0.010 | 0.049 | 0.081 | 0.854 | Yes | Yes | Yes |
| chr14:104019758-104028214 | RP11-894P9.2 | GTEx Thyroid | -5.462560 | 4.69e-08 | 0.000 | 0.001 | 0.000 | 0.005 | 0.994 | Yes | Yes | Yes |
| chr14:104153913-104154464 | RP11-73M18.6 | PsychENCODE | 5.031320 | 4.87e-07 | 0.000 | 0.005 | 0.001 | 0.413 | 0.581 | No | No | No |
| chr14:104160897-104161507 | RP11-73M18.7 | PsychENCODE | 4.856130 | 1.20e-06 | 0.000 | 0.006 | 0.000 | 0.513 | 0.480 | No | No | No |
| chr14:104162690-104163500 | RP11-73M18.8 | GTEx Amygdala | 5.142000 | 2.72e-07 | 0.010 | 0.002 | 0.082 | 0.019 | 0.887 | Yes | Yes | Yes |
| chr14:104177607-104179149 | AL049840.1 | GTEx Cerebellum | 5.029540 | 4.92e-07 | 0.001 | 0.003 | 0.008 | 0.026 | 0.962 | Yes | Yes | Yes |
| chr14:104177607-104179149 | AL049840.1 | GTEx Cortex | 5.143620 | 2.69e-07 | 0.001 | 0.002 | 0.007 | 0.012 | 0.979 | Yes | Yes | Yes |
| chr14:104179904-104180441 | RP11-73M18.9 | GTEx Cortex | 4.977330 | 6.45e-07 | 0.000 | 0.002 | 0.001 | 0.013 | 0.984 | Yes | Yes | Yes |
| chr14:104179904-104180586 | RP11-73M18.9 | PsychENCODE | 4.830100 | 1.36e-06 | 0.001 | 0.005 | 0.049 | 0.425 | 0.520 | No | No | No |
| chr16:72146056-72210777 | PMFBP1 | PsychENCODE | -5.160620 | 2.46e-07 | 0.013 | 0.005 | 0.174 | 0.069 | 0.738 | Yes | No | No |
| chr17:27400528-27507430 | MYO18A | GTEx Adrenal Gland | -5.128570 | 2.92e-07 | 0.002 | 0.040 | 0.001 | 0.019 | 0.937 | Yes | Yes | Yes |
| chr17:27401933-27405875 | TIAF1 | GTEx Adrenal Gland | -5.361200 | 8.27e-08 | 0.016 | 0.111 | 0.008 | 0.055 | 0.810 | Yes | Yes | Yes |
| chr17:65520597-65521538 | CTD-2653B5.1 | PsychENCODE | 5.105730 | 3.30e-07 | 0.000 | 0.441 | 0.000 | 0.028 | 0.531 | Yes | No | No |
| chr18:52385091-52562747 | RAB27B | PsychENCODE | 5.012900 | 5.36e-07 | 0.000 | 0.015 | 0.000 | 0.328 | 0.657 | No | No | No |
| chr18:52495707-52562747 | RAB27B | CMC DLPFC Splicing | 4.843190 | 1.28e-06 | 0.000 | 0.016 | 0.000 | 0.038 | 0.945 | Yes | Yes | Yes |
| chr20:47835831-47860614 | DDX27 | CMC DLPFC | 4.836260 | 1.32e-06 | 0.003 | 0.067 | 0.001 | 0.029 | 0.900 | Yes | Yes | Yes |
| chr22:41165634-41215403 | SLC25A17 | GTEx Nucleus accumbens | 5.076990 | 3.83e-07 | 0.007 | 0.001 | 0.547 | 0.097 | 0.348 | Yes | No | No |
| chr22:41165634-41215403 | SLC25A17 | GTEx Thyroid | 4.896100 | 9.78e-07 | 0.000 | 0.008 | 0.020 | 0.671 | 0.301 | No | No | No |
| chr22:41253088-41351450 | XPNPEP3 | GTEx Frontal Cortex | 4.951000 | 7.38e-07 | 0.009 | 0.001 | 0.731 | 0.091 | 0.168 | Yes | No | No |
| chr22:41258260-41363888 | XPNPEP3 | CMC DLPFC | 5.110000 | 3.21e-07 | 0.000 | 0.004 | 0.005 | 0.358 | 0.632 | No | No | No |
| chr22:41487790-41576081 | EP300 | GTEx Cerebellum | 5.493900 | 3.93e-08 | 0.001 | 0.001 | 0.061 | 0.049 | 0.888 | Yes | Yes | Yes |
| chr22:41487790-41576081 | EP300 | YFS Blood | 5.059100 | 4.21e-07 | 0.000 | 0.012 | 0.000 | 0.955 | 0.033 | No | No | No |
| chr22:41641614-41682216 | RANGAP1 | CMC DLPFC Splicing | 5.240100 | 1.61e-07 | 0.010 | 0.000 | 0.814 | 0.028 | 0.147 | Yes | No | No |
| chr22:41641615-41682255 | RANGAP1 | PsychENCODE | -5.575273 | 2.47e-08 | 0.000 | 0.004 | 0.000 | 0.705 | 0.290 | No | No | No |
| chr22:41697526-41756151 | ZC3H7B | GTEx Cerebellum | 5.729100 | 1.01e-08 | 0.001 | 0.000 | 0.105 | 0.031 | 0.862 | Yes | Yes | Yes |
140 of the 176 significant features presented a low posterior probability of TWAS and GWAS associations resulting from distinct causal SNPs. This is a good index of colocalisation, but PP4, should also be considered due to the possibility of other models besides PP3 and PP4 to be the most probable. When considering features with high PP4 (> 0.8), 97 of the 140 features with low PP3 also presented a high probability of GWAS and TWAS associations resulting from the same causal SNP. Therefore, based on both criteria applied, we considered 97 features as colocalised.
3.6 Process conditional analysis results
Organise coloc results
# Read in the report files
library(data.table)
setwd("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional")
# Read in the clean TWAS results
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
twas_sign$PANEL_clean<-gsub(' $','',twas_sign$PANEL_clean)
# Read in all jointly significant associations
temp = list.files(pattern=glob2rx("*chr*.report"))
report<-do.call(rbind, lapply(temp, function(x) read.table(x, header=T,stringsAsFactors=F)))
report$JOINT.ID<-NA
report$MARGIN.ID<-NA
report$JOINT.N<-NA
report$MARGIN.N<-NA
report$loc<-gsub('.*loc_','',report$FILE)
joint_res<-NULL
margin_res<-NULL
# Insert names of jointly significant genes
for(i in unique(report$CHR)){
joint_i<-read.table(paste0('test.cond.chr',i,'.joint_included.dat'), header=T,stringsAsFactors=F)
margin_i<-read.table(paste0('test.cond.chr',i,'.joint_dropped.dat'), header=T,stringsAsFactors=F)
joint_i$path<-gsub('/[^/]+$','',joint_i$FILE)
joint_i$path<-gsub('/[^/]+$','',joint_i$path)
joint_i$WGT<-NA
for(j in 1:dim(joint_i)[1]){
joint_i$WGT[j]<-gsub(paste0(joint_i$path[j],'/'),'',joint_i$FILE[j])
}
if(dim(margin_i)[1] > 0){
margin_i$path<-gsub('/[^/]+$','',margin_i$FILE)
margin_i$path<-gsub('/[^/]+$','',margin_i$path)
margin_i$WGT<-NA
for(j in 1:dim(margin_i)[1]){
margin_i$WGT[j]<-gsub(paste0(margin_i$path[j],'/'),'',margin_i$FILE[j])
}
}
temp = list.files(pattern=glob2rx(paste0("*chr",i,".loc*.genes")))
for(k in 1:length(temp)){
loc_k<-read.table(paste0('test.cond.chr',i,'.loc_',k,'.genes'), header=T, stringsAsFactors=F)
loc_k$path<-gsub('/[^/]+$','',loc_k$FILE)
loc_k$path<-gsub('/[^/]+$','',loc_k$path)
loc_k$WGT<-NA
for(j in 1:dim(loc_k)[1]){
loc_k$WGT[j]<-gsub(paste0(loc_k$path[j],'/'),'',loc_k$FILE[j])
}
loc_k$P0<-NULL
loc_k$P1<-NULL
loc_k<-merge(loc_k, twas_sign[,c('P0','P1','WGT','PANEL_clean')], by='WGT')
loc_k_joint<-loc_k[(loc_k$WGT %in% joint_i$WGT),]
joint_res<-rbind(joint_res,loc_k_joint)
if(dim(margin_i)[1] > 0){
loc_k_margin<-loc_k[(loc_k$WGT %in% margin_i$WGT),]
margin_res<-rbind(margin_res,loc_k_margin)
}
g_list<-NULL
for(g in unique(loc_k_joint$ID)){
g_list<-c(g_list,paste0(g, " (",paste(loc_k_joint$PANEL_clean[loc_k_joint$ID == g], collapse=', '),")"))
}
report[report$CHR == i & report$loc == k,]$JOINT.ID<-paste(g_list,collapse=', ')
if(dim(loc_k_margin)[1] > 0){
g_list<-NULL
for(g in unique(loc_k_margin$ID)){
g_list<-c(g_list,paste0(g, " (",paste(unique(loc_k_margin$PANEL_clean[loc_k_margin$ID == g]), collapse=', '),")"))
}
report[report$CHR == i & report$loc == k,]$MARGIN.ID<-paste(g_list,collapse=', ')
} else {
report[report$CHR == i & report$loc == k,]$MARGIN.ID<-'-'
}
report[report$CHR == i & report$loc == k,]$JOINT.N<-dim(loc_k_joint)[1]
report[report$CHR == i & report$loc == k,]$MARGIN.N<-dim(loc_k_margin)[1]
}
}
report$LOCUS<-paste0(report$CHR,':',report$P0,':',report$P1)
report$BP<-paste0(report$P0,'-',report$P1)
report$VAR.EXP<-paste0(report$VAR.EXP*100,'%')
report<-report[,c('CHR','P0','P1','BP','LOCUS',"JOINT.N",'MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP','JOINT.ID','MARGIN.ID')]
report<-report[order(report$CHR, report$P0),]
# Save full conditional results table
write.csv(report[,c("CHR","BP","JOINT.ID","MARGIN.ID","BEST.TWAS.P","BEST.SNP.P","VAR.EXP")],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_full.csv', row.names=F, quote=T)
# Save brief conditional results table
write.csv(report[,c('CHR','BP','JOINT.ID','MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP')],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_brief.csv', row.names=F, quote=T)
# Combine gene results for marginal and joint genes
joint_res$Type<-'Joint'
margin_res$Type<-'Marginal'
gene_res<-rbind(joint_res, margin_res)
# Check number of indepenent associations
dim(joint_res) # 50
# Check number of independent associations without genome-wide significant snp
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 25
# Check number of independent associations with genome-wide significant snp but an r2 with predicted expression <0.1
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1,]) # 2
# Check number of independent novel associations
dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 27
# Check number of novel associations
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8,]) # 68
gene_res$Novel<-'No'
gene_res$Novel[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8]<-'Yes'
gene_res$BP<-paste0(gene_res$P0,'-',gene_res$P1)
gene_res$BEST.GWAS.P<-2*pnorm(-abs(gene_res$BEST.GWAS.Z))
gene_res<-gene_res[order(gene_res$CHR, gene_res$P0),]
gene_res$Colocalised<-F
gene_res$Colocalised[gene_res$COLOC.PP4 >0.8]<-T
# Check number of independent novel associations which colocalise for joint genes
joint_res$Colocalised<-F
joint_res$Colocalised[joint_res$COLOC.PP4 >0.8]<-T
dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1 & joint_res$Colocalised == T) | (2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8 & joint_res$Colocalised == T),]) # 12
# Check number of novel associations which colocalise
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1 & gene_res$Colocalised == T) | (2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8 & gene_res$Colocalised == T),]) # 45
gene_res<-gene_res[,c('CHR','BP','ID','PANEL_clean','WGT','TWAS.P','BEST.GWAS.P','TOP.SNP.COR','Type','Novel','COLOC.PP3','COLOC.PP4','Colocalised')]
# Save table showing whether gene associations are novel
write.csv(gene_res,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv', row.names=F, quote=T)Show full conditional analysis table
| CHR | BP | Jointly sign. Features (SNP-weight set) | Marginally sign. Features (SNP-weight set) | Top TWAS p-value | Top GWAS p-value | Variance Explained |
|---|---|---|---|---|---|---|
| 1 | 7413452-9875347 | RERE (YFS Blood) | RP5-1115A15.1 (GTEx Thyroid, GTEx Whole Blood), RERE (GTEx Whole Blood) | 1.10e-07 | 3.18e-08 | 100% |
| 1 | 35885799-37876701 | SNORA63 (GTEx Nucleus accumbens) | - | 1.24e-06 | 6.27e-08 | 69.3% |
| 1 | 71753372-73766162 | NEGR1 (GTEx Whole Blood) | NEGR1 (GTEx Caudate, GTEx Putamen), RPL31P12 (GTEx Cerebellar Hemisphere, GTEx Cerebellum, PsychENCODE) | 1.94e-18 | 4.54e-15 | 97.8% |
| 1 | 174891875-177103690 | RFWD2 (CMC DLPFC Splicing) | RFWD2 (CMC DLPFC Splicing), RP11-318C24.2 (GTEx Thyroid) | 4.66e-07 | 2.30e-07 | 90% |
| 1 | 180725304-182724241 | CACNA1E (CMC DLPFC Splicing) | - | 6.06e-07 | 1.08e-07 | 64% |
| 1 | 196478918-198741422 | DENND1B (CMC DLPFC Splicing) | DENND1B (CMC DLPFC Splicing, CMC DLPFC) | 5.90e-08 | 3.11e-08 | 92.6% |
| 2 | 57388379-59467945 | FANCL (CMC DLPFC Splicing, CMC DLPFC) | - | 2.18e-07 | 4.68e-09 | 85% |
| 2 | 196832647-199295649 | ANKRD44 (YFS Blood) | SF3B1 (GTEx Hypothalamus) | 1.27e-08 | 3.52e-07 | 82.8% |
| 3 | 43487406-45561063 | ZNF445 (CMC DLPFC) | EP300 (GTEx Cerebellum, YFS Blood), XPNPEP3 (GTEx Frontal Cortex, CMC DLPFC), SLC25A17 (GTEx Nucleus accumbens, GTEx Thyroid), RANGAP1 (CMC DLPFC Splicing, PsychENCODE) | 3.34e-07 | 6.34e-08 | 74.7% |
| 4 | 40937584-43090938 | SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) | SLC30A9 (GTEx Amygdala, GTEx ACC, GTEx Caudate, GTEx Hypothalamus, GTEx Nucleus accumbens, PsychENCODE), RP11-814H16.2 (GTEx Cerebellar Hemisphere), DCAF4L1 (GTEx Thyroid) | 7.72e-09 | 3.59e-09 | 92.3% |
| 5 | 86565927-88989352 | TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) | TMEM161B-AS1 (GTEx Adrenal Gland, GTEx Amygdala, GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cerebellum, GTEx Cortex, GTEx Frontal Cortex, GTEx Hypothalamus, GTEx Nucleus accumbens, GTEx Putamen, GTEx Substantia nigra, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), CTC-467M3.3 (GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cortex, PsychENCODE) | 1.33e-12 | 1.07e-10 | -2250% |
| 5 | 139030460-141219083 | PCDHA5 (GTEx Thyroid) | PCDHA8 (GTEx Cerebellar Hemisphere), NDUFA2 (CMC DLPFC) | 6.55e-08 | 1.37e-06 | 87.7% |
| 6 | 25193720-29216321 | ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) | RP5-874C20.3 (GTEx Adrenal Gland, GTEx Cerebellum, GTEx Hippocampus, GTEx Putamen, GTEx Thyroid, GTEx Whole Blood), ZNF204P (GTEx Adrenal Gland), ZKSCAN3 (GTEx Amygdala, GTEx Hippocampus, GTEx Thyroid), ZSCAN31 (GTEx Amygdala), PGBD1 (GTEx Cerebellar Hemisphere), BTN3A2 (GTEx Cerebellar Hemisphere, GTEx Hippocampus, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), PRSS16 (GTEx Cerebellum, GTEx Frontal Cortex, GTEx Whole Blood), HMGN4 (GTEx Cerebellum), ZSCAN9 (GTEx Hippocampus, GTEx Pituitary), RP1-265C24.5 (GTEx Hippocampus), RP1-153G14.4 (GTEx Hippocampus), ZNF184 (GTEx Hypothalamus), ZSCAN23 (GTEx Putamen, GTEx Pituitary), HIST1H4D (NTR Blood), HIST1H4L (NTR Blood), NKAPL (PsychENCODE), ZSCAN12P1 (GTEx Whole Blood), ZSCAN16 (YFS Blood) | 2.53e-10 | 1.34e-10 | 100% |
| 6 | 30577966-32580366 | BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) | PPP1R18 (GTEx Adrenal Gland), FLOT1 (GTEx Cerebellum, CMC DLPFC Splicing, GTEx Pituitary, GTEx Thyroid), VARS2 (GTEx Cortex, GTEx Whole Blood), DDAH2 (GTEx Frontal Cortex, CMC DLPFC), WASF5P (GTEx Pituitary) | 1.55e-10 | 3.95e-08 | 86% |
| 6 | 98832858-100829135 | COQ3 (CMC DLPFC Splicing) | - | 2.65e-07 | 9.09e-08 | 35.1% |
| 6 | 104405706-106583999 | BVES-AS1 (GTEx Amygdala) | LIN28B (CMC DLPFC, PsychENCODE) | 2.43e-08 | 9.50e-08 | 92.9% |
| 7 | 11252396-13282905 | TMEM106B (PsychENCODE) | TMEM106B (GTEx Adrenal Gland, GTEx Whole Blood, YFS Blood) | 7.01e-09 | 2.55e-08 | 100% |
| 7 | 24021857-26019767 | OSBPL3 (GTEx Pituitary) | - | 1.88e-08 | 6.49e-07 | 77.7% |
| 8 | 51238261-53720740 | PXDNL (CMC DLPFC) | - | 3.92e-09 | 1.34e-07 | 83.8% |
| 8 | 60435234-62428932 | RP11-163N6.2 (GTEx Thyroid) | - | 9.47e-08 | 5.25e-07 | 89.8% |
| 9 | 125606617-127604411 | PIGFP2 (PsychENCODE) | - | 1.12e-07 | 2.73e-08 | 63.8% |
| 11 | 56092913-58422547 | TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) | AP000662.4 (GTEx Thyroid) | 2.04e-07 | 1.47e-07 | 95.2% |
| 11 | 60540194-62557903 | TMEM258 (PsychENCODE) | - | 5.12e-07 | 4.26e-07 | 83.9% |
| 11 | 112346414-114345882 | DRD2 (GTEx Frontal Cortex) | - | 3.90e-07 | 4.90e-07 | 0.414% |
| 13 | 52652520-54625616 | OLFM4 (CMC DLPFC) | - | 3.56e-07 | 6.06e-19 | 29.9% |
| 14 | 41077086-43073683 | CTD-2298J14.2 (GTEx Thyroid) | LRFN5 (GTEx Cerebellar Hemisphere, GTEx Cerebellum) | 1.36e-08 | 2.57e-09 | 88.1% |
| 14 | 58952573-61334943 | CCDC175 (GTEx Thyroid) | RTN1 (CMC DLPFC Splicing, GTEx Thyroid), JKAMP (GTEx Thyroid) | 4.28e-08 | 2.18e-07 | 82.3% |
| 14 | 63322572-65770213 | ESR2 (GTEx Pituitary) | SYNE2 (NTR Blood), ESR2 (GTEx Whole Blood) | 2.20e-09 | 7.60e-10 | 80% |
| 14 | 74120633-76388050 | PROX2 (GTEx Thyroid) | RPS6KL1 (CMC DLPFC Splicing, PsychENCODE), DLST (CMC DLPFC, PsychENCODE), AREL1 (PsychENCODE) | 8.51e-09 | 6.71e-09 | 93.5% |
| 14 | 102878783-105180229 | RP11-894P9.2 (GTEx Thyroid) | RP11-73M18.8 (GTEx Amygdala), AL049840.1 (GTEx Cerebellum, GTEx Cortex), RP11-73M18.9 (GTEx Cortex, PsychENCODE), TRMT61A (CMC DLPFC, GTEx Whole Blood), RP11-600F24.2 (PsychENCODE), RP11-73M18.7 (PsychENCODE), RP11-73M18.6 (PsychENCODE), CKB (YFS Blood) | 4.69e-08 | 3.05e-09 | 84.6% |
| 16 | 71147494-73210261 | PMFBP1 (PsychENCODE) | RP11-73M18.8 (GTEx Amygdala), AL049840.1 (GTEx Cerebellum, GTEx Cortex), RP11-73M18.9 (GTEx Cortex, PsychENCODE), TRMT61A (CMC DLPFC, GTEx Whole Blood), RP11-600F24.2 (PsychENCODE), RP11-73M18.7 (PsychENCODE), RP11-73M18.6 (PsychENCODE), CKB (YFS Blood) | 2.46e-07 | 3.35e-08 | 76.3% |
| 17 | 26406423-28478661 | TIAF1 (GTEx Adrenal Gland) | MYO18A (GTEx Adrenal Gland) | 8.27e-08 | 8.51e-09 | 58.5% |
| 17 | 64524284-66521332 | CTD-2653B5.1 (PsychENCODE) | - | 3.30e-07 | 5.39e-06 | 25.8% |
| 18 | 51385406-53561919 | RAB27B (PsychENCODE) | RAB27B (CMC DLPFC Splicing) | 5.36e-07 | 3.62e-11 | 14.6% |
| 20 | 46838019-48853908 | DDX27 (CMC DLPFC) | SF3B1 (GTEx Hypothalamus) | 1.32e-06 | 3.54e-06 | 91% |
| 22 | 40218102-42697216 | ZC3H7B (GTEx Cerebellum) | EP300 (GTEx Cerebellum, YFS Blood), XPNPEP3 (GTEx Frontal Cortex, CMC DLPFC), SLC25A17 (GTEx Nucleus accumbens, GTEx Thyroid), RANGAP1 (CMC DLPFC Splicing, PsychENCODE) | 1.01e-08 | 7.56e-09 | 95.5% |
Show brief conditional analysis table
| CHR | BP | Jointly sign. Features (SNP-weight set) | N Marginal | Top TWAS p-value | Top GWAS p-value | Variance Explained |
|---|---|---|---|---|---|---|
| 1 | 7413452-9875347 | RERE (YFS Blood) | 3 | 1.10e-07 | 3.18e-08 | 100% |
| 1 | 35885799-37876701 | SNORA63 (GTEx Nucleus accumbens) | 0 | 1.24e-06 | 6.27e-08 | 69.3% |
| 1 | 71753372-73766162 | NEGR1 (GTEx Whole Blood) | 5 | 1.94e-18 | 4.54e-15 | 97.8% |
| 1 | 174891875-177103690 | RFWD2 (CMC DLPFC Splicing) | 3 | 4.66e-07 | 2.30e-07 | 90% |
| 1 | 180725304-182724241 | CACNA1E (CMC DLPFC Splicing) | 0 | 6.06e-07 | 1.08e-07 | 64% |
| 1 | 196478918-198741422 | DENND1B (CMC DLPFC Splicing) | 2 | 5.90e-08 | 3.11e-08 | 92.6% |
| 2 | 57388379-59467945 | FANCL (CMC DLPFC Splicing, CMC DLPFC) | 0 | 2.18e-07 | 4.68e-09 | 85% |
| 2 | 196832647-199295649 | ANKRD44 (YFS Blood) | 1 | 1.27e-08 | 3.52e-07 | 82.8% |
| 3 | 43487406-45561063 | ZNF445 (CMC DLPFC) | 8 | 3.34e-07 | 6.34e-08 | 74.7% |
| 4 | 40937584-43090938 | SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) | 8 | 7.72e-09 | 3.59e-09 | 92.3% |
| 5 | 86565927-88989352 | TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) | 18 | 1.33e-12 | 1.07e-10 | -2250% |
| 5 | 139030460-141219083 | PCDHA5 (GTEx Thyroid) | 2 | 6.55e-08 | 1.37e-06 | 87.7% |
| 6 | 25193720-29216321 | ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) | 33 | 2.53e-10 | 1.34e-10 | 100% |
| 6 | 30577966-32580366 | BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) | 12 | 1.55e-10 | 3.95e-08 | 86% |
| 6 | 98832858-100829135 | COQ3 (CMC DLPFC Splicing) | 0 | 2.65e-07 | 9.09e-08 | 35.1% |
| 6 | 104405706-106583999 | BVES-AS1 (GTEx Amygdala) | 2 | 2.43e-08 | 9.50e-08 | 92.9% |
| 7 | 11252396-13282905 | TMEM106B (PsychENCODE) | 3 | 7.01e-09 | 2.55e-08 | 100% |
| 7 | 24021857-26019767 | OSBPL3 (GTEx Pituitary) | 0 | 1.88e-08 | 6.49e-07 | 77.7% |
| 8 | 51238261-53720740 | PXDNL (CMC DLPFC) | 0 | 3.92e-09 | 1.34e-07 | 83.8% |
| 8 | 60435234-62428932 | RP11-163N6.2 (GTEx Thyroid) | 0 | 9.47e-08 | 5.25e-07 | 89.8% |
| 9 | 125606617-127604411 | PIGFP2 (PsychENCODE) | 0 | 1.12e-07 | 2.73e-08 | 63.8% |
| 11 | 56092913-58422547 | TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) | 1 | 2.04e-07 | 1.47e-07 | 95.2% |
| 11 | 60540194-62557903 | TMEM258 (PsychENCODE) | 0 | 5.12e-07 | 4.26e-07 | 83.9% |
| 11 | 112346414-114345882 | DRD2 (GTEx Frontal Cortex) | 0 | 3.90e-07 | 4.90e-07 | 0.414% |
| 13 | 52652520-54625616 | OLFM4 (CMC DLPFC) | 0 | 3.56e-07 | 6.06e-19 | 29.9% |
| 14 | 41077086-43073683 | CTD-2298J14.2 (GTEx Thyroid) | 2 | 1.36e-08 | 2.57e-09 | 88.1% |
| 14 | 58952573-61334943 | CCDC175 (GTEx Thyroid) | 3 | 4.28e-08 | 2.18e-07 | 82.3% |
| 14 | 63322572-65770213 | ESR2 (GTEx Pituitary) | 2 | 2.20e-09 | 7.60e-10 | 80% |
| 14 | 74120633-76388050 | PROX2 (GTEx Thyroid) | 5 | 8.51e-09 | 6.71e-09 | 93.5% |
| 14 | 102878783-105180229 | RP11-894P9.2 (GTEx Thyroid) | 11 | 4.69e-08 | 3.05e-09 | 84.6% |
| 16 | 71147494-73210261 | PMFBP1 (PsychENCODE) | 11 | 2.46e-07 | 3.35e-08 | 76.3% |
| 17 | 26406423-28478661 | TIAF1 (GTEx Adrenal Gland) | 1 | 8.27e-08 | 8.51e-09 | 58.5% |
| 17 | 64524284-66521332 | CTD-2653B5.1 (PsychENCODE) | 0 | 3.30e-07 | 5.39e-06 | 25.8% |
| 18 | 51385406-53561919 | RAB27B (PsychENCODE) | 1 | 5.36e-07 | 3.62e-11 | 14.6% |
| 20 | 46838019-48853908 | DDX27 (CMC DLPFC) | 1 | 1.32e-06 | 3.54e-06 | 91% |
| 22 | 40218102-42697216 | ZC3H7B (GTEx Cerebellum) | 8 | 1.01e-08 | 7.56e-09 | 95.5% |
Show novelty table
| CHR | BP | ID | PANEL | WGT | TWAS.P | BEST.GWAS.P | TOP.SNP.COR | TYPE | NOVEL | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat | RERE | 1 | 0.08894 | rs301819 | 5.510 | rs301806 | 0.153527 | -13.99 | 5.18200 | 521 | 4 | lasso | 0.157000 | 0.00e+00 | -5.310078 | 1.10e-07 | 0.000 | 0.001 | 0.000 | 0.006 | 0.993 | TRUE | -0.97 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 8412457 | 8877702 | YFS Blood | Joint | No | 8412457-8877702 | 3.588337e-08 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat | RERE | 1 | 0.24580 | rs301819 | 5.510 | rs301806 | 0.175000 | -8.47 | 5.18200 | 376 | 4 | lasso | 0.173923 | 3.89e-17 | -5.095707 | 3.47e-07 | 0.000 | 0.001 | 0.000 | 0.006 | 0.993 | FALSE | -0.98 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 8412457 | 8877702 | GTEx Whole Blood | Marginal | No | 8412457-8877702 | 3.588337e-08 | TRUE |
| Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat | RP5-1115A15.1 | 1 | 0.11740 | rs301819 | 5.510 | rs301805 | 0.087900 | -6.47 | 5.36500 | 306 | 4 | lasso | 0.100301 | 6.06e-11 | -5.175240 | 2.28e-07 | 0.000 | 0.001 | 0.000 | 0.004 | 0.995 | FALSE | -0.94 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 8484705 | 8494898 | GTEx Thyroid | Marginal | No | 8484705-8494898 | 3.588337e-08 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat | RP5-1115A15.1 | 1 | 0.05580 | rs301819 | 5.510 | rs301805 | -0.002120 | -3.68 | 5.36500 | 306 | 306 | blup | 0.006669 | 6.35e-02 | -4.866386 | 1.14e-06 | 0.012 | 0.001 | 0.088 | 0.008 | 0.891 | FALSE | -0.75 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 8484705 | 8494898 | GTEx Whole Blood | Marginal | No | 8484705-8494898 | 3.588337e-08 | TRUE |
| Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat | Brain_Nucleus_accumbens_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat | SNORA63 | 1 | 0.19900 | rs1002656 | 5.380 | rs7544015 | -0.007660 | 3.62 | 3.19800 | 371 | 371 | blup | 0.006930 | 1.71e-01 | 4.848870 | 1.24e-06 | 0.067 | 0.006 | 0.257 | 0.023 | 0.647 | TRUE | 0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia | 36884051 | 36884179 | GTEx Nucleus accumbens | Joint | Yes | 36884051-36884179 | 7.448584e-08 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat | NEGR1 | 1 | 0.13050 | rs7531118 | -7.810 | rs11209948 | 0.069200 | -5.78 | -7.60100 | 319 | 15 | enet | 0.112708 | 2.29e-11 | 8.760622 | 1.94e-18 | 0.000 | 0.000 | 0.000 | 0.007 | 0.993 | TRUE | -0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 71861623 | 72748417 | GTEx Whole Blood | Joint | No | 71861623-72748417 | 5.718799e-15 | TRUE |
| Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat | Brain_Caudate_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat | NEGR1 | 1 | 0.16780 | rs7531118 | -7.810 | rs12759396 | 0.001590 | -3.72 | -6.11000 | 319 | 319 | blup | 0.055669 | 2.64e-03 | 5.780100 | 7.47e-09 | 0.000 | 0.000 | 0.284 | 0.036 | 0.681 | FALSE | -0.69 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia | 71861623 | 72748417 | GTEx Caudate | Marginal | No | 71861623-72748417 | 5.718799e-15 | FALSE |
| Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat | Brain_Putamen_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat | NEGR1 | 1 | 0.30300 | rs7531118 | -7.810 | rs2012697 | 0.194919 | -4.83 | -7.51000 | 318 | 20 | enet | 0.251490 | 1.44e-08 | 5.548510 | 2.88e-08 | 0.000 | 0.000 | 0.018 | 0.014 | 0.968 | FALSE | -0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia | 71861623 | 72748417 | GTEx Putamen | Marginal | No | 71861623-72748417 | 5.718799e-15 | TRUE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat | RPL31P12 | 1 | 0.46500 | rs7531118 | -7.810 | rs11209948 | 0.524000 | 8.05 | -7.60100 | 321 | 6 | lasso | 0.522525 | 1.56e-21 | -7.785520 | 6.94e-15 | 0.000 | 0.000 | 0.000 | 0.010 | 0.990 | FALSE | 0.99 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 72767155 | 72767512 | GTEx Cerebellar Hemisphere | Marginal | No | 72767155-72767512 | 5.718799e-15 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat | RPL31P12 | 1 | 0.63030 | rs7531118 | -7.810 | rs2568958 | 0.567000 | 9.31 | -7.74100 | 321 | 3 | lasso | 0.544830 | 8.24e-28 | -7.708820 | 1.27e-14 | 0.000 | 0.000 | 0.000 | 0.006 | 0.994 | FALSE | 1.00 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 72767155 | 72767512 | GTEx Cerebellum | Marginal | No | 72767155-72767512 | 5.718799e-15 | TRUE |
| PEC_TWAS_weights/ENSG00000227207.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000227207.wgt.RDat | RPL31P12 | 1 | 0.17714 | rs1432639 | -7.839 | rs2568960 | 0.225000 | 17.46 | -7.76900 | 1184 | 10 | enet | 0.227000 | 0.00e+00 | -7.742756 | 9.73e-15 | 0.000 | 0.000 | 0.000 | 0.007 | 0.993 | FALSE | 0.99 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 72767155 | 72767512 | PsychENCODE | Marginal | No | 72767155-72767512 | 4.541485e-15 | TRUE |
| Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat | RP11-318C24.2 | 1 | 0.04890 | rs10913112 | -5.170 | rs6680839 | 0.037700 | -4.51 | 4.60700 | 394 | 3 | lasso | 0.041100 | 2.73e-05 | -5.027510 | 4.97e-07 | 0.004 | 0.007 | 0.012 | 0.018 | 0.959 | FALSE | 0.88 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 175873898 | 175889649 | GTEx Thyroid | Marginal | Yes | 175873898-175889649 | 2.340940e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat | RFWD2 | 1 | 0.13900 | rs10913112 | -5.170 | rs10436856 | 0.231000 | -10.23 | -5.01400 | 366 | 3 | lasso | 0.253891 | 4.06e-30 | 5.039850 | 4.66e-07 | 0.000 | 0.007 | 0.000 | 0.020 | 0.973 | TRUE | -0.86 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 175913966 | 176176370 | CMC DLPFC Splicing | Joint | Yes | 175913966-176176370 | 2.340940e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat | RFWD2 | 1 | 0.13430 | rs10913112 | -5.170 | rs10753117 | 0.209000 | 9.69 | -4.72600 | 365 | 4 | lasso | 0.210674 | 1.10e-24 | -4.958690 | 7.10e-07 | 0.000 | 0.008 | 0.000 | 0.021 | 0.971 | FALSE | 0.93 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 175913966 | 176176370 | CMC DLPFC Splicing | Marginal | Yes | 175913966-176176370 | 2.340940e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat | RFWD2 | 1 | 0.12220 | rs10913112 | -5.170 | rs10436856 | 0.168000 | 9.29 | -5.01400 | 356 | 4 | lasso | 0.214117 | 4.15e-25 | -5.005960 | 5.56e-07 | 0.000 | 0.007 | 0.000 | 0.019 | 0.973 | FALSE | 0.84 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 175913966 | 176176370 | CMC DLPFC Splicing | Marginal | Yes | 175913966-176176370 | 2.340940e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat | CACNA1E | 1 | 0.11150 | rs2332571 | 5.310 | rs4652678 | 0.083800 | -6.59 | 3.70200 | 490 | 4 | lasso | 0.086921 | 1.48e-10 | -4.989390 | 6.06e-07 | 0.000 | 0.151 | 0.000 | 0.420 | 0.429 | TRUE | -0.52 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 181452685 | 181775921 | CMC DLPFC Splicing | Joint | Yes | 181452685-181775921 | 1.096252e-07 | FALSE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat | DENND1B | 1 | 0.15900 | rs16841842 | -5.350 | rs17641524 | 0.180000 | 9.05 | -5.29200 | 338 | 6 | lasso | 0.172047 | 4.55e-20 | -5.421950 | 5.90e-08 | 0.000 | 0.001 | 0.000 | 0.010 | 0.989 | TRUE | 0.90 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 197473878 | 197744623 | CMC DLPFC Splicing | Joint | Yes | 197473878-197744623 | 8.795423e-08 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat | DENND1B | 1 | 0.10260 | rs16841842 | -5.350 | rs16841904 | 0.153000 | -8.49 | -5.21100 | 354 | 8 | lasso | 0.144111 | 7.46e-17 | 5.018050 | 5.22e-07 | 0.000 | 0.001 | 0.000 | 0.010 | 0.989 | FALSE | -0.89 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 197473878 | 197744623 | CMC DLPFC Splicing | Marginal | Yes | 197473878-197744623 | 8.795423e-08 | TRUE |
| CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat | DENND1B | 1 | 0.07270 | rs16841842 | -5.350 | rs16841842 | 0.050100 | -5.76 | -5.34600 | 417 | 417 | blup | 0.055662 | 2.33e-07 | 4.848374 | 1.24e-06 | 0.000 | 0.001 | 0.000 | 0.011 | 0.988 | FALSE | -0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 197473878 | 197744623 | CMC DLPFC | Marginal | Yes | 197473878-197744623 | 8.795423e-08 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat | FANCL | 2 | 0.06710 | rs11682175 | 5.860 | rs10172295 | 0.033604 | 4.78 | 4.38500 | 347 | 347 | blup | 0.040895 | 1.07e-05 | 4.897476 | 9.71e-07 | 0.000 | 0.001 | 0.007 | 0.104 | 0.888 | TRUE | 0.52 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 58386377 | 58468515 | CMC DLPFC Splicing | Joint | No | 58386377-58468515 | 4.628672e-09 | TRUE |
| CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat | FANCL | 2 | 0.06110 | rs11682175 | 5.860 | rs11682175 | 0.008634 | -3.97 | 5.85800 | 387 | 387 | blup | 0.029378 | 1.48e-04 | -5.183180 | 2.18e-07 | 0.001 | 0.000 | 0.056 | 0.027 | 0.916 | TRUE | -0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 58386377 | 58468515 | CMC DLPFC | Joint | No | 58386377-58468515 | 4.628672e-09 | TRUE |
| YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat | ANKRD44 | 2 | 0.02219 | rs10931791 | 4.940 | rs2256931 | -0.000728 | -3.70 | 1.89600 | 416 | 416 | blup | 0.008445 | 6.29e-04 | -5.690140 | 1.27e-08 | 0.062 | 0.023 | 0.168 | 0.061 | 0.686 | TRUE | -0.60 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 197831741 | 198175897 | YFS Blood | Joint | Yes | 197831741-198175897 | 7.812257e-07 | FALSE |
| Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat | Brain_Hypothalamus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat | SF3B1 | 2 | 0.18700 | rs7557203 | 5.030 | rs700655 | 0.021790 | 3.90 | 4.26100 | 328 | 328 | blup | 0.064130 | 4.90e-03 | 5.214900 | 1.84e-07 | 0.071 | 0.015 | 0.319 | 0.068 | 0.526 | FALSE | 0.81 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus | 198254508 | 198299815 | GTEx Hypothalamus | Marginal | Yes | 198254508-198299815 | 4.904798e-07 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat | ZNF445 | 3 | 0.06220 | rs11707582 | 5.330 | rs7616113 | 0.035714 | -5.55 | 3.61700 | 353 | 353 | blup | 0.060170 | 7.65e-08 | -5.103280 | 3.34e-07 | 0.000 | 0.087 | 0.002 | 0.601 | 0.310 | TRUE | -0.66 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 44481261 | 44561226 | CMC DLPFC | Joint | Yes | 44481261-44561226 | 9.821277e-08 | FALSE |
| PEC_TWAS_weights/ENSG00000109133.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000109133.wgt.RDat | TMEM33 | 4 | 0.09793 | rs13146152 | -5.590 | rs9990708 | 0.031300 | -7.50 | -5.28500 | 1527 | 14 | enet | 0.054006 | 7.05e-18 | 4.837418 | 1.32e-06 | 0.000 | 0.001 | 0.000 | 0.074 | 0.925 | TRUE | -0.42 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 41937137 | 41962589 | PsychENCODE | Joint | No | 41937137-41962589 | 2.270696e-08 | TRUE |
| Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat | DCAF4L1 | 4 | 0.06810 | rs16854051 | -5.900 | rs4861156 | 0.044500 | 4.39 | -5.12800 | 423 | 1 | lasso | 0.032600 | 1.73e-04 | -5.128000 | 2.93e-07 | 0.003 | 0.001 | 0.291 | 0.091 | 0.615 | FALSE | 0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 41983713 | 41988476 | GTEx Thyroid | Marginal | No | 41983713-41988476 | 3.635016e-09 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat | RP11-814H16.2 | 4 | 0.16500 | rs16854051 | -5.900 | rs6848386 | 0.035484 | -3.58 | -4.79600 | 424 | 5 | lasso | 0.044330 | 1.08e-02 | 5.009600 | 5.45e-07 | 0.005 | 0.000 | 0.561 | 0.055 | 0.378 | FALSE | -0.74 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 41990758 | 41991254 | GTEx Cerebellar Hemisphere | Marginal | No | 41990758-41991254 | 3.635016e-09 | FALSE |
| Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.27030 | rs16854051 | -5.900 | rs4377621 | 0.016520 | 3.94 | -4.91100 | 424 | 424 | blup | 0.063090 | 1.92e-03 | -5.774530 | 7.72e-09 | 0.003 | 0.001 | 0.340 | 0.113 | 0.543 | TRUE | 0.73 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 41992489 | 42092474 | GTEx Cortex | Joint | No | 41992489-42092474 | 3.635016e-09 | FALSE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.28400 | rs16854051 | -5.900 | rs1507086 | -0.011800 | 3.52 | -4.45600 | 424 | 424 | blup | 0.064540 | 1.00e-02 | -5.253400 | 1.49e-07 | 0.004 | 0.001 | 0.476 | 0.070 | 0.450 | FALSE | 0.79 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 41992489 | 42092474 | GTEx Amygdala | Marginal | No | 41992489-42092474 | 3.635016e-09 | FALSE |
| Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat | Brain_Anterior_cingulate_cortex_BA24 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.15160 | rs16854051 | -5.900 | rs4377621 | 0.074069 | 3.86 | -4.91100 | 424 | 424 | blup | 0.053870 | 8.95e-03 | -5.001690 | 5.68e-07 | 0.003 | 0.001 | 0.399 | 0.101 | 0.496 | FALSE | 0.81 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 | 41992489 | 42092474 | GTEx ACC | Marginal | No | 41992489-42092474 | 3.635016e-09 | FALSE |
| Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat | Brain_Caudate_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.14720 | rs16854051 | -5.900 | rs1507086 | 0.018708 | 3.85 | -4.45600 | 424 | 3 | enet | 0.013800 | 8.61e-02 | -4.854800 | 1.21e-06 | 0.003 | 0.001 | 0.388 | 0.086 | 0.521 | FALSE | 0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia | 41992489 | 42092474 | GTEx Caudate | Marginal | No | 41992489-42092474 | 3.635016e-09 | FALSE |
| Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat | Brain_Hypothalamus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.27600 | rs16854051 | -5.900 | rs4861163 | 0.113100 | 4.68 | -5.13000 | 424 | 424 | blup | 0.146040 | 2.89e-05 | -5.085140 | 3.67e-07 | 0.000 | 0.000 | 0.016 | 0.039 | 0.944 | FALSE | 0.83 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus | 41992489 | 42092474 | GTEx Hypothalamus | Marginal | No | 41992489-42092474 | 3.635016e-09 | TRUE |
| Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat | Brain_Nucleus_accumbens_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat | SLC30A9 | 4 | 0.24800 | rs16854051 | -5.900 | rs1983138 | 0.071800 | 4.58 | -4.88700 | 424 | 424 | blup | 0.080020 | 6.80e-04 | -5.602700 | 2.11e-08 | 0.001 | 0.001 | 0.072 | 0.119 | 0.808 | FALSE | 0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia | 41992489 | 42092474 | GTEx Nucleus accumbens | Marginal | No | 41992489-42092474 | 3.635016e-09 | TRUE |
| PEC_TWAS_weights/ENSG00000014824.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000014824.wgt.RDat | SLC30A9 | 4 | 0.28664 | rs13146152 | -5.590 | rs4861157 | 0.122000 | 12.78 | -5.55500 | 1626 | 43 | enet | 0.233893 | 0.00e+00 | -5.259200 | 1.45e-07 | 0.000 | 0.000 | 0.000 | 0.026 | 0.974 | FALSE | 0.76 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 41992489 | 42092474 | PsychENCODE | Marginal | No | 41992489-42092474 | 2.270696e-08 | TRUE |
| PEC_TWAS_weights/ENSG00000247828.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000247828.wgt.RDat | TMEM161B-AS1 | 5 | 0.30403 | rs27732 | 6.371 | rs112055376 | 0.293000 | 19.82 | 6.03823 | 1178 | 1178 | bslmm | 0.303307 | 0.00e+00 | 6.091010 | 1.12e-09 | 0.000 | 0.000 | 0.000 | 0.117 | 0.883 | TRUE | 0.57 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 87564712 | 87732502 | PsychENCODE | Joint | No | 87564712-87732502 | 1.877996e-10 | TRUE |
| Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat | Brain_Caudate_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.28690 | rs10044618 | 6.390 | rs780404 | 0.302955 | 6.60 | 6.02700 | 259 | 11 | lasso | 0.269805 | 1.80e-11 | 6.282167 | 3.34e-10 | 0.000 | 0.000 | 0.000 | 0.062 | 0.938 | FALSE | 0.67 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia | 87564888 | 87732502 | GTEx Caudate | Joint | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.31160 | rs10044618 | 6.390 | rs415302 | 0.269660 | 7.18 | 5.49800 | 274 | 11 | lasso | 0.286037 | 1.76e-14 | 5.360090 | 8.32e-08 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | FALSE | 0.52 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 87564888 | 87732502 | GTEx Adrenal Gland | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.38600 | rs10044618 | 6.390 | rs780404 | 0.287340 | 5.49 | 6.02700 | 256 | 10 | lasso | 0.278270 | 9.13e-08 | 6.118500 | 9.45e-10 | 0.000 | 0.000 | 0.004 | 0.058 | 0.938 | FALSE | 0.58 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 87564888 | 87732502 | GTEx Amygdala | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat | Brain_Anterior_cingulate_cortex_BA24 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.37620 | rs10044618 | 6.390 | rs390856 | 0.306000 | 6.21 | 6.00300 | 256 | 9 | lasso | 0.317250 | 1.33e-10 | 6.445500 | 1.15e-10 | 0.000 | 0.000 | 0.000 | 0.056 | 0.944 | FALSE | 0.69 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 | 87564888 | 87732502 | GTEx ACC | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.33450 | rs10044618 | 6.390 | rs780404 | 0.233104 | 6.00 | 6.02700 | 257 | 10 | lasso | 0.233430 | 7.93e-09 | 6.011700 | 1.84e-09 | 0.000 | 0.000 | 0.000 | 0.062 | 0.938 | TRUE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 87564888 | 87732502 | GTEx Cerebellar Hemisphere | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.36980 | rs10044618 | 6.390 | rs780404 | 0.320000 | 7.24 | 6.02700 | 263 | 4 | lasso | 0.323363 | 1.06e-14 | 6.053050 | 1.42e-09 | 0.000 | 0.000 | 0.000 | 0.052 | 0.948 | FALSE | 0.58 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 87564888 | 87732502 | GTEx Cerebellum | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.38200 | rs10044618 | 6.390 | rs11743103 | 0.319615 | 6.92 | 5.98300 | 258 | 11 | lasso | 0.337950 | 8.68e-14 | 6.021420 | 1.73e-09 | 0.000 | 0.000 | 0.000 | 0.070 | 0.930 | FALSE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 87564888 | 87732502 | GTEx Cortex | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.30390 | rs10044618 | 6.390 | rs780404 | 0.287968 | 6.15 | 6.02700 | 259 | 24 | enet | 0.295280 | 1.47e-10 | 6.720000 | 1.82e-11 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | FALSE | 0.70 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 87564888 | 87732502 | GTEx Frontal Cortex | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat | Brain_Hypothalamus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.39800 | rs10044618 | 6.390 | rs780404 | 0.336574 | 6.07 | 6.02700 | 255 | 5 | lasso | 0.313540 | 2.17e-10 | 5.875800 | 4.21e-09 | 0.000 | 0.000 | 0.000 | 0.060 | 0.940 | FALSE | 0.57 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus | 87564888 | 87732502 | GTEx Hypothalamus | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat | Brain_Nucleus_accumbens_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.33100 | rs10044618 | 6.390 | rs780404 | 0.266183 | 6.16 | 6.02700 | 257 | 8 | lasso | 0.266900 | 2.21e-10 | 6.010490 | 1.85e-09 | 0.000 | 0.000 | 0.000 | 0.059 | 0.941 | FALSE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia | 87564888 | 87732502 | GTEx Nucleus accumbens | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat | Brain_Putamen_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.45800 | rs10044618 | 6.390 | rs780404 | 0.332685 | 6.08 | 6.02700 | 257 | 8 | lasso | 0.285111 | 1.14e-09 | 6.372050 | 1.87e-10 | 0.000 | 0.000 | 0.000 | 0.054 | 0.946 | FALSE | 0.65 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia | 87564888 | 87732502 | GTEx Putamen | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat | Brain_Substantia_nigra | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra/Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.46000 | rs10044618 | 6.390 | rs780404 | 0.333000 | 5.41 | 6.02700 | 256 | 6 | lasso | 0.323370 | 2.75e-08 | 6.057270 | 1.38e-09 | 0.000 | 0.000 | 0.009 | 0.054 | 0.937 | FALSE | 0.60 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra | 87564888 | 87732502 | GTEx Substantia nigra | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.42890 | rs10044618 | 6.390 | rs13172095 | 0.434966 | 8.35 | 6.05800 | 260 | 2 | lasso | 0.427610 | 1.30e-20 | 6.048500 | 1.46e-09 | 0.000 | 0.000 | 0.000 | 0.050 | 0.950 | FALSE | 0.58 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 87564888 | 87732502 | GTEx Pituitary | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.20080 | rs10044618 | 6.390 | rs13172095 | 0.223000 | 9.66 | 6.05800 | 274 | 7 | lasso | 0.229150 | 2.09e-24 | 5.889760 | 3.87e-09 | 0.000 | 0.000 | 0.000 | 0.079 | 0.920 | FALSE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 87564888 | 87732502 | GTEx Thyroid | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat | TMEM161B-AS1 | 5 | 0.12240 | rs10044618 | 6.390 | rs4916899 | 0.042902 | 5.54 | 6.01100 | 274 | 6 | lasso | 0.063570 | 5.72e-07 | 5.526440 | 3.27e-08 | 0.000 | 0.000 | 0.000 | 0.048 | 0.952 | TRUE | 0.53 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 87564888 | 87732502 | GTEx Whole Blood | Marginal | No | 87564888-87732502 | 1.658858e-10 | TRUE |
| Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat | Brain_Substantia_nigra | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra/Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat | CTC-498M16.4 | 5 | 0.20700 | rs10044618 | 6.390 | rs10044618 | 0.005520 | 3.82 | 6.38700 | 255 | 255 | blup | 0.055980 | 2.02e-02 | 5.403610 | 6.53e-08 | 0.000 | 0.000 | 0.391 | 0.037 | 0.572 | TRUE | 0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra | 87729709 | 87794514 | GTEx Substantia nigra | Joint | No | 87729709-87794514 | 1.658858e-10 | FALSE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat | CTC-467M3.3 | 5 | 0.18240 | rs10044618 | 6.390 | rs1081158 | 0.013998 | -4.20 | 6.19200 | 267 | 267 | blup | 0.052300 | 7.53e-03 | -7.091600 | 1.33e-12 | 0.000 | 0.000 | 0.114 | 0.035 | 0.850 | TRUE | -0.45 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 87988462 | 87989789 | GTEx Frontal Cortex | Joint | No | 87988462-87989789 | 1.658858e-10 | TRUE |
| Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat | Brain_Anterior_cingulate_cortex_BA24 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat | CTC-467M3.3 | 5 | 0.21470 | rs10044618 | 6.390 | rs2304607 | 0.019300 | -3.67 | 5.78200 | 266 | 266 | blup | 0.113220 | 2.18e-04 | -5.813700 | 6.11e-09 | 0.000 | 0.000 | 0.631 | 0.053 | 0.317 | FALSE | -0.67 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 | 87988462 | 87989789 | GTEx ACC | Marginal | No | 87988462-87989789 | 1.658858e-10 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat | CTC-467M3.3 | 5 | 0.12920 | rs10044618 | 6.390 | rs454214 | 0.019687 | -4.57 | 6.22200 | 266 | 5 | lasso | 0.021460 | 5.68e-02 | -5.861000 | 4.60e-09 | 0.000 | 0.000 | 0.056 | 0.051 | 0.893 | FALSE | -0.30 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 87988462 | 87989789 | GTEx Cerebellar Hemisphere | Marginal | Yes | 87988462-87989789 | 1.658858e-10 | TRUE |
| Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat | CTC-467M3.3 | 5 | 0.22120 | rs10044618 | 6.390 | rs34338 | 0.068029 | -4.29 | 6.30000 | 267 | 21 | enet | 0.068860 | 1.23e-03 | -6.510990 | 7.47e-11 | 0.000 | 0.000 | 0.091 | 0.040 | 0.870 | FALSE | -0.41 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 87988462 | 87989789 | GTEx Cortex | Marginal | No | 87988462-87989789 | 1.658858e-10 | TRUE |
| PEC_TWAS_weights/ENSG00000250377.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000250377.wgt.RDat | CTC-467M3.3 | 5 | 0.02825 | rs27732 | 6.371 | rs7733438 | 0.006110 | -5.04 | 5.77100 | 841 | 3 | lasso | 0.012112 | 3.61e-05 | -6.097890 | 1.07e-09 | 0.000 | 0.000 | 0.035 | 0.251 | 0.715 | FALSE | -0.12 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 87988462 | 87989789 | PsychENCODE | Marginal | Yes | 87988462-87989789 | 1.877996e-10 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat | NDUFA2 | 5 | 0.01750 | rs3806843 | -4.830 | rs12659980 | 0.007610 | -3.56 | -4.53300 | 343 | 343 | bslmm | 0.013400 | 7.81e-03 | 5.190020 | 2.10e-07 | 0.104 | 0.037 | 0.170 | 0.060 | 0.629 | FALSE | -0.86 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 140024947 | 140027370 | CMC DLPFC | Marginal | Yes | 140024947-140027370 | 1.365331e-06 | FALSE |
| Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat | PCDHA5 | 5 | 0.06360 | rs3806843 | -4.830 | rs2098058 | 0.004860 | 3.84 | -4.61500 | 376 | 14 | enet | 0.022560 | 1.55e-03 | -5.402970 | 6.55e-08 | 0.055 | 0.028 | 0.090 | 0.046 | 0.781 | TRUE | 0.66 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 140201222 | 140203811 | GTEx Thyroid | Joint | Yes | 140201222-140203811 | 1.365331e-06 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat | PCDHA8 | 5 | 0.22650 | rs3806843 | -4.830 | rs2563265 | 0.121994 | 5.37 | -4.50500 | 372 | 372 | blup | 0.180230 | 5.33e-07 | -4.980100 | 6.36e-07 | 0.001 | 0.032 | 0.001 | 0.051 | 0.915 | FALSE | 0.86 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 140220907 | 140223351 | GTEx Cerebellar Hemisphere | Marginal | Yes | 140220907-140223351 | 1.365331e-06 | TRUE |
| NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat | NTR.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat | HIST1H4D | 6 | 0.01730 | rs3799380 | -6.350 | rs16891464 | 0.000459 | -3.71 | 0.97800 | 634 | 634 | bslmm | 0.005940 | 3.75e-03 | -4.987600 | 6.11e-07 | 0.000 | 0.000 | 0.309 | 0.059 | 0.632 | FALSE | 0.42 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR | 26188921 | 26189323 | NTR Blood | Marginal | No | 26188921-26189323 | 2.153149e-10 | FALSE |
| NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat | NTR.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat | BTN3A2 | 6 | 0.39320 | rs3799380 | -6.350 | rs9379851 | 0.511828 | -25.26 | -5.44400 | 532 | 35 | enet | 0.517690 | 0.00e+00 | 5.326600 | 1.00e-07 | 0.000 | 0.000 | 0.000 | 0.562 | 0.438 | FALSE | -0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR | 26365386 | 26378540 | NTR Blood | Joint | No | 26365386-26378540 | 2.153149e-10 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat | BTN3A2 | 6 | 0.52900 | rs3799380 | -6.350 | rs9366653 | 0.348070 | -6.68 | -5.40600 | 557 | 15 | lasso | 0.311590 | 9.83e-12 | 5.188200 | 2.12e-07 | 0.000 | 0.000 | 0.000 | 0.505 | 0.495 | FALSE | -0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 26365387 | 26378546 | GTEx Cerebellar Hemisphere | Marginal | No | 26365387-26378546 | 2.153149e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat | BTN3A2 | 6 | 0.56000 | rs3799380 | -6.350 | rs9366653 | 0.245340 | -6.29 | -5.40600 | 557 | 24 | lasso | 0.318160 | 8.38e-11 | 4.963000 | 6.96e-07 | 0.000 | 0.000 | 0.000 | 0.506 | 0.494 | FALSE | -0.62 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 26365387 | 26378546 | GTEx Hippocampus | Marginal | No | 26365387-26378546 | 2.153149e-10 | FALSE |
| Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat | BTN3A2 | 6 | 0.56000 | rs3799380 | -6.350 | rs9366653 | 0.421860 | -8.20 | -5.40600 | 555 | 21 | lasso | 0.427690 | 1.28e-20 | 5.898930 | 3.66e-09 | 0.000 | 0.000 | 0.000 | 0.501 | 0.499 | FALSE | -0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 26365387 | 26378546 | GTEx Pituitary | Marginal | No | 26365387-26378546 | 2.153149e-10 | FALSE |
| Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat | BTN3A2 | 6 | 0.73560 | rs3799380 | -6.350 | rs9366653 | 0.563545 | -15.23 | -5.40600 | 557 | 40 | enet | 0.595520 | 0.00e+00 | 5.481600 | 4.22e-08 | 0.000 | 0.000 | 0.000 | 0.507 | 0.492 | FALSE | -0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 26365387 | 26378546 | GTEx Thyroid | Marginal | No | 26365387-26378546 | 2.153149e-10 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat | BTN3A2 | 6 | 0.64660 | rs3799380 | -6.350 | rs9379851 | 0.500493 | -14.04 | -5.44400 | 557 | 35 | enet | 0.541880 | 0.00e+00 | 5.086960 | 3.64e-07 | 0.000 | 0.000 | 0.000 | 0.543 | 0.457 | FALSE | -0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 26365387 | 26378546 | GTEx Whole Blood | Marginal | No | 26365387-26378546 | 2.153149e-10 | FALSE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat | HMGN4 | 6 | 0.17300 | rs3799380 | -6.350 | rs9393729 | 0.020800 | 3.93 | 4.08200 | 486 | 486 | blup | 0.012600 | 8.82e-02 | 5.395400 | 6.84e-08 | 0.000 | 0.000 | 0.404 | 0.299 | 0.297 | FALSE | -0.32 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 26538633 | 26546482 | GTEx Cerebellum | Marginal | No | 26538633-26546482 | 2.153149e-10 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat | PRSS16 | 6 | 0.40500 | rs6938943 | -6.310 | rs13219354 | 0.171270 | 5.12 | -4.22300 | 339 | 19 | enet | 0.139750 | 1.13e-05 | -4.891200 | 1.00e-06 | 0.000 | 0.000 | 0.096 | 0.322 | 0.582 | FALSE | 0.56 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 27215480 | 27224250 | GTEx Cerebellar Hemisphere | Joint | No | 27215480-27224250 | 2.790355e-10 | FALSE |
| Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat | PRSS16 | 6 | 0.12900 | rs6938943 | -6.310 | rs4713096 | 0.089290 | 4.21 | -5.84100 | 340 | 6 | lasso | 0.073530 | 3.62e-04 | -5.916080 | 3.30e-09 | 0.000 | 0.000 | 0.044 | 0.035 | 0.921 | FALSE | 0.30 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 27215480 | 27224250 | GTEx Pituitary | Joint | Yes | 27215480-27224250 | 2.790355e-10 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat | PRSS16 | 6 | 0.34700 | rs6938943 | -6.310 | rs13219354 | 0.193950 | 6.56 | -4.22300 | 340 | 4 | lasso | 0.234300 | 1.39e-10 | -4.947900 | 7.50e-07 | 0.000 | 0.001 | 0.000 | 0.962 | 0.037 | FALSE | 0.55 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 27215480 | 27224250 | GTEx Cerebellum | Marginal | No | 27215480-27224250 | 2.790355e-10 | FALSE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat | PRSS16 | 6 | 0.18900 | rs6938943 | -6.310 | rs9348772 | 0.046080 | 3.93 | -3.08200 | 341 | 18 | enet | 0.055850 | 5.93e-03 | -5.045000 | 4.54e-07 | 0.000 | 0.000 | 0.366 | 0.056 | 0.577 | FALSE | 0.48 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 27215480 | 27224250 | GTEx Frontal Cortex | Marginal | No | 27215480-27224250 | 2.790355e-10 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat | PRSS16 | 6 | 0.08520 | rs6938943 | -6.310 | rs6913660 | 0.059488 | 5.11 | -5.15500 | 342 | 4 | lasso | 0.043950 | 2.99e-05 | -5.335920 | 9.51e-08 | 0.000 | 0.001 | 0.047 | 0.474 | 0.479 | FALSE | 0.47 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 27215480 | 27224250 | GTEx Whole Blood | Marginal | No | 27215480-27224250 | 2.790355e-10 | FALSE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat | ZNF204P | 6 | 0.14100 | rs6938943 | -6.310 | rs201004 | 0.059999 | 4.34 | -4.72200 | 395 | 7 | lasso | 0.050060 | 1.74e-03 | -5.032700 | 4.84e-07 | 0.000 | 0.000 | 0.547 | 0.211 | 0.242 | FALSE | 0.60 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 27325604 | 27339304 | GTEx Adrenal Gland | Marginal | No | 27325604-27339304 | 2.790355e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat | RP1-153G14.4 | 6 | 0.15900 | rs6938943 | -6.310 | rs10946940 | 0.040750 | -3.92 | -4.17000 | 410 | 410 | blup | 0.051320 | 9.89e-03 | 5.354000 | 8.60e-08 | 0.000 | 0.000 | 0.676 | 0.142 | 0.182 | FALSE | -0.28 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 27371789 | 27374743 | GTEx Hippocampus | Marginal | Yes | 27371789-27374743 | 2.790355e-10 | FALSE |
| Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat | Brain_Caudate_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat | ZNF184 | 6 | 0.11600 | rs6938943 | -6.310 | rs7509 | 0.072807 | 4.25 | -4.56300 | 433 | 433 | blup | 0.080900 | 3.39e-04 | -6.325200 | 2.53e-10 | 0.000 | 0.000 | 0.231 | 0.114 | 0.655 | TRUE | 0.53 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia | 27418522 | 27440897 | GTEx Caudate | Joint | No | 27418522-27440897 | 2.790355e-10 | FALSE |
| Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat | Brain_Hypothalamus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat | ZNF184 | 6 | 0.27900 | rs6938943 | -6.310 | rs13207082 | 0.140380 | 4.83 | -5.23300 | 434 | 18 | enet | 0.139570 | 4.38e-05 | -4.952200 | 7.34e-07 | 0.000 | 0.000 | 0.174 | 0.185 | 0.641 | FALSE | 0.66 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus | 27418522 | 27440897 | GTEx Hypothalamus | Marginal | No | 27418522-27440897 | 2.790355e-10 | FALSE |
| NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat | NTR.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat | HIST1H4L | 6 | 0.02310 | rs853676 | -6.390 | rs13218875 | 0.014479 | -5.55 | -5.43800 | 470 | 470 | bslmm | 0.013000 | 3.30e-05 | 4.870800 | 1.11e-06 | 0.000 | 0.000 | 0.001 | 0.162 | 0.838 | FALSE | -0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR | 27840926 | 27841289 | NTR Blood | Marginal | No | 27840926-27841289 | 1.658858e-10 | TRUE |
| PEC_TWAS_weights/ENSG00000219891.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000219891.wgt.RDat | ZSCAN12P1 | 6 | 0.06139 | rs6905391 | -6.420 | rs144436694 | 0.010266 | 4.94 | 4.09400 | 671 | 671 | bslmm | 0.030670 | 8.82e-11 | 6.268010 | 3.66e-10 | 0.000 | 0.000 | 0.056 | 0.626 | 0.318 | FALSE | -0.49 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 28058932 | 28061442 | PsychENCODE | Joint | No | 28058932-28061442 | 1.362743e-10 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat | ZSCAN12P1 | 6 | 0.03660 | rs853676 | -6.390 | rs1225591 | 0.012007 | 4.01 | -4.93300 | 481 | 481 | blup | 0.013440 | 1.48e-02 | -4.936930 | 7.94e-07 | 0.000 | 0.000 | 0.388 | 0.365 | 0.247 | FALSE | 0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 28058932 | 28061442 | GTEx Whole Blood | Marginal | No | 28058932-28061442 | 1.658858e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat | RP1-265C24.5 | 6 | 0.21100 | rs853676 | -6.390 | rs203888 | 0.141810 | -5.09 | -3.81500 | 476 | 33 | enet | 0.179240 | 2.42e-06 | 5.532000 | 3.16e-08 | 0.000 | 0.000 | 0.009 | 0.143 | 0.847 | FALSE | -0.73 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 28083406 | 28084329 | GTEx Hippocampus | Marginal | No | 28083406-28084329 | 1.658858e-10 | TRUE |
| YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat | ZSCAN16 | 6 | 0.01350 | rs853676 | -6.390 | rs853685 | 0.005153 | 4.29 | -6.29200 | 417 | 417 | blup | 0.014070 | 1.40e-05 | -6.109000 | 1.00e-09 | 0.000 | 0.000 | 0.013 | 0.054 | 0.933 | FALSE | 0.80 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 28092338 | 28097860 | YFS Blood | Marginal | No | 28092338-28097860 | 1.658858e-10 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat | ZSCAN9 | 6 | 0.12000 | rs853676 | -6.390 | rs13197574 | 0.032570 | 4.17 | -5.61800 | 443 | 443 | blup | 0.040600 | 7.15e-03 | -5.307800 | 1.11e-07 | 0.000 | 0.000 | 0.238 | 0.147 | 0.615 | FALSE | 0.73 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 28192664 | 28201260 | GTEx Cerebellum | Joint | No | 28192664-28201260 | 1.658858e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat | ZSCAN9 | 6 | 0.14200 | rs853676 | -6.390 | rs17750424 | 0.082780 | 4.19 | -5.43700 | 443 | 28 | enet | 0.103880 | 3.50e-04 | -6.017000 | 1.77e-09 | 0.000 | 0.000 | 0.080 | 0.053 | 0.866 | FALSE | 0.65 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 28192664 | 28201260 | GTEx Hippocampus | Marginal | No | 28192664-28201260 | 1.658858e-10 | TRUE |
| Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat | ZSCAN9 | 6 | 0.20000 | rs853676 | -6.390 | rs13197574 | 0.039090 | 4.35 | -5.61800 | 443 | 22 | enet | 0.062970 | 9.22e-04 | -6.159020 | 7.32e-10 | 0.000 | 0.000 | 0.169 | 0.191 | 0.640 | FALSE | 0.70 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 28192664 | 28201260 | GTEx Pituitary | Marginal | No | 28192664-28201260 | 1.658858e-10 | FALSE |
| PEC_TWAS_weights/ENSG00000189134.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000189134.wgt.RDat | NKAPL | 6 | 0.02766 | rs6905391 | -6.420 | rs112863641 | 0.008266 | 4.31 | 3.55700 | 621 | 621 | bslmm | 0.007610 | 8.74e-04 | 5.002860 | 5.65e-07 | 0.000 | 0.000 | 0.627 | 0.332 | 0.041 | FALSE | -0.45 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 28227098 | 28228736 | PsychENCODE | Marginal | No | 28227098-28228736 | 1.362743e-10 | FALSE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.18070 | rs853676 | -6.390 | rs1778508 | 0.040376 | -4.63 | -5.77400 | 426 | 426 | blup | 0.103630 | 8.80e-06 | 5.094600 | 3.49e-07 | 0.000 | 0.000 | 0.031 | 0.146 | 0.822 | FALSE | -0.72 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 28234788 | 28245974 | GTEx Adrenal Gland | Marginal | No | 28234788-28245974 | 1.658858e-10 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.47500 | rs853676 | -6.390 | rs213237 | 0.240770 | -6.61 | -2.86200 | 425 | 425 | blup | 0.314800 | 2.75e-14 | 5.062800 | 4.13e-07 | 0.000 | 0.000 | 0.000 | 0.756 | 0.244 | FALSE | -0.80 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 28234788 | 28245974 | GTEx Cerebellum | Marginal | No | 28234788-28245974 | 1.658858e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.27000 | rs853676 | -6.390 | rs16894095 | 0.072900 | -4.28 | -2.29100 | 425 | 425 | blup | 0.114230 | 1.80e-04 | 5.198000 | 2.01e-07 | 0.000 | 0.000 | 0.172 | 0.174 | 0.654 | FALSE | -0.69 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 28234788 | 28245974 | GTEx Hippocampus | Marginal | No | 28234788-28245974 | 1.658858e-10 | FALSE |
| Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat | Brain_Putamen_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.33900 | rs853676 | -6.390 | rs13408 | 0.035181 | -4.74 | -3.40000 | 425 | 13 | enet | 0.119100 | 1.31e-04 | 5.739000 | 9.52e-09 | 0.000 | 0.000 | 0.018 | 0.051 | 0.931 | FALSE | -0.84 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia | 28234788 | 28245974 | GTEx Putamen | Marginal | No | 28234788-28245974 | 1.658858e-10 | TRUE |
| Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.22340 | rs853676 | -6.390 | rs13408 | 0.220783 | -9.47 | -3.40000 | 426 | 10 | lasso | 0.281390 | 1.75e-30 | 5.338400 | 9.38e-08 | 0.000 | 0.000 | 0.000 | 0.858 | 0.141 | FALSE | -0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 28234788 | 28245974 | GTEx Thyroid | Marginal | No | 28234788-28245974 | 1.658858e-10 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat | RP5-874C20.3 | 6 | 0.04100 | rs853676 | -6.390 | rs1778508 | 0.005820 | -3.82 | -5.77400 | 426 | 426 | blup | 0.008980 | 3.82e-02 | 5.662330 | 1.49e-08 | 0.000 | 0.000 | 0.288 | 0.107 | 0.604 | FALSE | -0.77 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 28234788 | 28245974 | GTEx Whole Blood | Marginal | No | 28234788-28245974 | 1.658858e-10 | FALSE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat | PGBD1 | 6 | 0.17900 | rs853676 | -6.390 | rs853685 | 0.067570 | 4.32 | -6.29200 | 418 | 6 | lasso | 0.076220 | 1.12e-03 | -6.313100 | 2.74e-10 | 0.000 | 0.000 | 0.032 | 0.017 | 0.950 | TRUE | 0.99 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 28249314 | 28270326 | GTEx Cerebellar Hemisphere | Marginal | No | 28249314-28270326 | 1.658858e-10 | TRUE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat | ZSCAN31 | 6 | 0.26700 | rs853676 | -6.390 | rs203876 | 0.155750 | 4.56 | -5.03400 | 382 | 7 | enet | 0.146500 | 1.49e-04 | -5.084150 | 3.69e-07 | 0.000 | 0.000 | 0.381 | 0.404 | 0.214 | FALSE | 0.57 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 28292470 | 28324048 | GTEx Amygdala | Marginal | No | 28292470-28324048 | 1.658858e-10 | FALSE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat | ZKSCAN3 | 6 | 0.16200 | rs853676 | -6.390 | rs1233708 | 0.060030 | -3.63 | -5.12500 | 383 | 383 | blup | 0.093000 | 2.37e-03 | 4.949900 | 7.43e-07 | 0.000 | 0.000 | 0.777 | 0.111 | 0.111 | FALSE | -0.51 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 28317691 | 28336947 | GTEx Amygdala | Marginal | No | 28317691-28336947 | 1.658858e-10 | FALSE |
| Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat | Brain_Hippocampus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat | ZKSCAN3 | 6 | 0.17600 | rs853676 | -6.390 | rs9393909 | 0.061440 | -4.54 | -5.08300 | 385 | 10 | lasso | 0.071750 | 2.71e-03 | 4.951000 | 7.37e-07 | 0.000 | 0.000 | 0.389 | 0.273 | 0.338 | FALSE | -0.74 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus | 28317691 | 28336947 | GTEx Hippocampus | Marginal | No | 28317691-28336947 | 1.658858e-10 | FALSE |
| Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat | ZKSCAN3 | 6 | 0.13670 | rs853676 | -6.390 | rs1233708 | 0.062905 | -6.08 | -5.12500 | 385 | 385 | blup | 0.102180 | 3.98e-11 | 6.093300 | 1.11e-09 | 0.000 | 0.000 | 0.000 | 0.086 | 0.914 | FALSE | -0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 28317691 | 28336947 | GTEx Thyroid | Marginal | No | 28317691-28336947 | 1.658858e-10 | TRUE |
| Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat | Brain_Hypothalamus | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat | ZSCAN23 | 6 | 0.21000 | rs853676 | -6.390 | rs1233708 | 0.130370 | 4.79 | -5.12500 | 342 | 342 | blup | 0.135790 | 5.57e-05 | -5.777500 | 7.58e-09 | 0.000 | 0.000 | 0.062 | 0.179 | 0.758 | FALSE | 0.74 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus | 28399707 | 28411279 | GTEx Hypothalamus | Joint | No | 28399707-28411279 | 1.658858e-10 | FALSE |
| Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat | Brain_Putamen_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat | ZSCAN23 | 6 | 0.22900 | rs853676 | -6.390 | rs9468317 | 0.101459 | 4.45 | -5.08800 | 342 | 342 | blup | 0.140400 | 3.27e-05 | -4.894000 | 9.90e-07 | 0.000 | 0.000 | 0.239 | 0.226 | 0.535 | FALSE | 0.70 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia | 28399707 | 28411279 | GTEx Putamen | Marginal | No | 28399707-28411279 | 1.658858e-10 | FALSE |
| Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat | ZSCAN23 | 6 | 0.22900 | rs853676 | -6.390 | rs916403 | 0.134260 | 5.75 | -3.46400 | 342 | 342 | blup | 0.179750 | 2.09e-08 | -4.953290 | 7.30e-07 | 0.000 | 0.000 | 0.002 | 0.275 | 0.723 | FALSE | 0.72 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 28399707 | 28411279 | GTEx Pituitary | Marginal | No | 28399707-28411279 | 1.658858e-10 | FALSE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat | PPP1R18 | 6 | 0.20040 | rs3130557 | -5.300 | rs2233956 | 0.046697 | -3.98 | -2.22200 | 77 | 77 | blup | 0.077590 | 1.17e-04 | 4.910200 | 9.10e-07 | 0.106 | 0.007 | 0.130 | 0.007 | 0.750 | FALSE | -0.02 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 30644166 | 30655672 | GTEx Adrenal Gland | Marginal | Yes | 30644166-30655672 | 1.158027e-07 | FALSE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat | FLOT1 | 6 | 0.28430 | rs3130557 | -5.300 | rs3130557 | 0.298470 | 11.52 | -5.29900 | 77 | 3 | lasso | 0.295823 | 0.00e+00 | -5.299700 | 1.16e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | FALSE | 0.43 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 30695485 | 30710682 | CMC DLPFC Splicing | Marginal | Yes | 30695485-30710682 | 1.158027e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat | FLOT1 | 6 | 0.21370 | rs3130557 | -5.300 | rs3130557 | 0.182560 | 9.15 | -5.29900 | 77 | 10 | lasso | 0.167113 | 1.71e-19 | -5.067100 | 4.04e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | FALSE | 0.43 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 30695485 | 30710682 | CMC DLPFC Splicing | Marginal | Yes | 30695485-30710682 | 1.158027e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat | FLOT1 | 6 | 0.14360 | rs3130557 | -5.300 | rs3130557 | 0.098890 | -7.05 | -5.29900 | 77 | 3 | lasso | 0.092157 | 4.05e-11 | 4.936600 | 7.95e-07 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | FALSE | -0.40 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 30695485 | 30710682 | CMC DLPFC Splicing | Marginal | Yes | 30695485-30710682 | 1.158027e-07 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat | FLOT1 | 6 | 0.18400 | rs3130557 | -5.300 | rs3130557 | 0.112620 | 4.38 | -5.29900 | 77 | 1 | lasso | 0.074900 | 3.62e-04 | -5.299000 | 1.16e-07 | 0.010 | 0.001 | 0.012 | 0.000 | 0.976 | FALSE | 0.42 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 30695486 | 30710510 | GTEx Cerebellum | Marginal | Yes | 30695486-30710510 | 1.158027e-07 | TRUE |
| Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat | FLOT1 | 6 | 0.22400 | rs3130557 | -5.300 | rs3130557 | 0.089150 | 4.23 | -5.29900 | 77 | 3 | lasso | 0.065390 | 7.44e-04 | -5.253270 | 1.49e-07 | 0.016 | 0.001 | 0.020 | 0.000 | 0.963 | FALSE | 0.41 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 30695486 | 30710510 | GTEx Pituitary | Marginal | Yes | 30695486-30710510 | 1.158027e-07 | TRUE |
| Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat | FLOT1 | 6 | 0.29080 | rs3130557 | -5.300 | rs3130557 | 0.132032 | 7.54 | -5.29900 | 77 | 4 | lasso | 0.144340 | 2.47e-15 | -5.557400 | 2.74e-08 | 0.000 | 0.001 | 0.000 | 0.000 | 0.999 | FALSE | 0.44 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 30695486 | 30710510 | GTEx Thyroid | Marginal | Yes | 30695486-30710510 | 1.158027e-07 | TRUE |
| Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat | VARS2 | 6 | 0.37500 | rs3130557 | -5.300 | rs1811197 | 0.114080 | -4.02 | -4.98000 | 95 | 5 | lasso | 0.098840 | 1.21e-04 | 5.922000 | 3.18e-09 | 0.105 | 0.004 | 0.158 | 0.005 | 0.727 | FALSE | -0.66 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 30881982 | 30894236 | GTEx Cortex | Marginal | Yes | 30881982-30894236 | 1.158027e-07 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat | VARS2 | 6 | 0.17210 | rs3130557 | -5.300 | rs3130557 | 0.046674 | -4.37 | -5.29900 | 95 | 95 | blup | 0.039720 | 7.00e-05 | 6.323130 | 2.56e-10 | 0.005 | 0.001 | 0.007 | 0.000 | 0.986 | FALSE | -0.60 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 30881982 | 30894236 | GTEx Whole Blood | Marginal | Yes | 30881982-30894236 | 1.158027e-07 | TRUE |
| Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat | WASF5P | 6 | 0.50500 | rs3130557 | -5.300 | rs2523578 | 0.236690 | 6.26 | -4.44300 | 207 | 207 | blup | 0.233340 | 1.02e-10 | -5.156240 | 2.52e-07 | 0.000 | 0.046 | 0.000 | 0.091 | 0.862 | FALSE | 0.54 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 31255287 | 31256741 | GTEx Pituitary | Marginal | Yes | 31255287-31256741 | 1.158027e-07 | TRUE |
| Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat | HCP5 | 6 | 0.09580 | rs3130557 | -5.300 | rs3094005 | 0.059439 | -5.62 | -5.01000 | 243 | 243 | blup | 0.051290 | 2.99e-06 | 6.400800 | 1.55e-10 | 0.000 | 0.008 | 0.000 | 0.015 | 0.976 | TRUE | -0.58 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 31368479 | 31445283 | GTEx Thyroid | Joint | Yes | 31368479-31445283 | 1.158027e-07 | TRUE |
| Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat | MICB | 6 | 0.46800 | rs3130557 | -5.300 | rs2534671 | 0.118617 | 8.29 | -4.63600 | 280 | 27 | enet | 0.322490 | 0.00e+00 | -5.557000 | 2.74e-08 | 0.000 | 0.047 | 0.000 | 0.094 | 0.859 | TRUE | 0.20 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 31462658 | 31478901 | GTEx Thyroid | Joint | Yes | 31462658-31478901 | 1.158027e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat | BAG6 | 6 | 0.09910 | rs3094005 | -5.010 | rs2239689 | 0.042150 | -5.27 | 2.38100 | 245 | 245 | blup | 0.052463 | 6.57e-07 | -5.580000 | 2.40e-08 | 0.008 | 0.408 | 0.006 | 0.319 | 0.259 | FALSE | 0.40 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 31606805 | 31620482 | CMC DLPFC Splicing | Joint | Yes | 31606805-31620482 | 5.443004e-07 | FALSE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat | DDAH2 | 6 | 0.32300 | rs3094005 | -5.010 | rs707938 | 0.119210 | -4.28 | -3.30800 | 242 | 242 | blup | 0.094280 | 4.46e-04 | 5.409500 | 6.32e-08 | 0.331 | 0.042 | 0.258 | 0.033 | 0.336 | FALSE | -0.30 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 31694815 | 31698357 | GTEx Frontal Cortex | Marginal | Yes | 31694815-31698357 | 5.443004e-07 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat | DDAH2 | 6 | 0.07420 | rs3094005 | -5.010 | rs1144708 | 0.042170 | -5.86 | -4.46600 | 245 | 245 | blup | 0.053500 | 3.92e-07 | 5.344500 | 9.07e-08 | 0.000 | 0.051 | 0.000 | 0.039 | 0.909 | FALSE | -0.46 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 31694816 | 31698039 | CMC DLPFC | Marginal | Yes | 31694816-31698039 | 5.443004e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat | COQ3 | 6 | 0.04000 | rs10457592 | 5.230 | rs2029965 | 0.011000 | -3.35 | -1.91300 | 474 | 474 | bslmm | 0.007760 | 3.54e-02 | 5.146560 | 2.65e-07 | 0.324 | 0.015 | 0.598 | 0.029 | 0.034 | TRUE | 0.22 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 99817347 | 99842082 | CMC DLPFC Splicing | Joint | Yes | 99817347-99842082 | 1.695100e-07 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat | LIN28B | 6 | 0.04180 | rs1475120 | -5.300 | rs4946651 | 0.038000 | 5.19 | -5.26100 | 434 | 9 | lasso | 0.034788 | 3.91e-05 | -5.232050 | 1.68e-07 | 0.000 | 0.001 | 0.000 | 0.008 | 0.990 | FALSE | 0.97 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 105404922 | 105531207 | CMC DLPFC | Marginal | Yes | 105404922-105531207 | 1.158027e-07 | TRUE |
| PEC_TWAS_weights/ENSG00000187772.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000187772.wgt.RDat | LIN28B | 6 | 0.06906 | rs370771 | -5.340 | rs13203645 | 0.044900 | -8.64 | 4.94900 | 1270 | 18 | enet | 0.061400 | 3.70e-20 | -5.105689 | 3.30e-07 | 0.000 | 0.005 | 0.000 | 0.052 | 0.943 | FALSE | 0.73 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 105404923 | 105531207 | PsychENCODE | Marginal | Yes | 105404923-105531207 | 9.294658e-08 | TRUE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat | BVES-AS1 | 6 | 0.34900 | rs1475120 | -5.300 | rs2153127 | 0.104990 | -3.89 | 4.60700 | 377 | 7 | lasso | 0.093320 | 2.33e-03 | -5.578300 | 2.43e-08 | 0.061 | 0.007 | 0.373 | 0.045 | 0.514 | TRUE | 0.81 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 105584224 | 105617820 | GTEx Amygdala | Joint | Yes | 105584224-105617820 | 1.158027e-07 | FALSE |
| PEC_TWAS_weights/ENSG00000106460.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000106460.wgt.RDat | TMEM106B | 7 | 0.06497 | rs3815535 | 5.440 | rs3800847 | 0.039600 | -7.91 | 5.36300 | 2571 | 6 | lasso | 0.041800 | 3.68e-14 | -5.790690 | 7.01e-09 | 0.000 | 0.001 | 0.000 | 0.054 | 0.945 | TRUE | -0.95 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 12250867 | 12282993 | PsychENCODE | Joint | Yes | 12250867-12282993 | 5.328057e-08 | TRUE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat | TMEM106B | 7 | 0.22000 | rs1990622 | 5.420 | rs6460900 | 0.126809 | 4.93 | 5.28500 | 672 | 3 | lasso | 0.084100 | 6.14e-05 | 5.505026 | 3.69e-08 | 0.000 | 0.001 | 0.003 | 0.009 | 0.987 | FALSE | 0.98 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 12250867 | 12282993 | GTEx Adrenal Gland | Marginal | Yes | 12250867-12282993 | 5.959904e-08 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat | TMEM106B | 7 | 0.38280 | rs1990622 | 5.420 | rs6460900 | 0.142440 | 7.73 | 5.28500 | 672 | 41 | enet | 0.201363 | 7.48e-20 | 5.531000 | 3.18e-08 | 0.000 | 0.001 | 0.000 | 0.008 | 0.991 | FALSE | 0.82 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 12250867 | 12282993 | GTEx Whole Blood | Marginal | Yes | 12250867-12282993 | 5.959904e-08 | TRUE |
| YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat | TMEM106B | 7 | 0.07763 | rs1990622 | 5.420 | rs5011432 | 0.055000 | 8.73 | 5.35000 | 677 | 7 | lasso | 0.055160 | 1.65e-17 | 5.373600 | 7.72e-08 | 0.000 | 0.001 | 0.000 | 0.007 | 0.993 | FALSE | 0.95 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 12250867 | 12276886 | YFS Blood | Marginal | Yes | 12250867-12276886 | 5.959904e-08 | TRUE |
| Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat | OSBPL3 | 7 | 0.32980 | rs10486432 | 4.960 | rs8180777 | 0.044100 | -4.25 | 0.96600 | 610 | 610 | blup | 0.129980 | 2.25e-06 | -5.622890 | 1.88e-08 | 0.090 | 0.040 | 0.062 | 0.027 | 0.780 | TRUE | -0.53 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 24836158 | 25021253 | GTEx Pituitary | Joint | Yes | 24836158-25021253 | 7.049318e-07 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat | PXDNL | 8 | 0.05330 | rs12548147 | 5.250 | rs7008730 | -0.000193 | 3.73 | 3.26800 | 586 | 586 | blup | 0.015150 | 5.06e-03 | 5.887460 | 3.92e-09 | 0.090 | 0.019 | 0.318 | 0.065 | 0.508 | TRUE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 52232136 | 52722005 | CMC DLPFC | Joint | Yes | 52232136-52722005 | 1.520992e-07 | FALSE |
| Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat | RP11-163N6.2 | 8 | 0.04980 | rs618190 | 5.020 | rs597123 | 0.011932 | -4.38 | 3.33300 | 402 | 402 | blup | 0.013830 | 1.08e-02 | -5.336530 | 9.47e-08 | 0.084 | 0.162 | 0.118 | 0.228 | 0.408 | TRUE | -0.74 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 61297147 | 61429354 | GTEx Thyroid | Joint | Yes | 61297147-61429354 | 5.167148e-07 | FALSE |
| PEC_TWAS_weights/ENSG00000234881.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000234881.wgt.RDat | PIGFP2 | 9 | 0.02070 | rs7029033 | 5.560 | rs667138 | 0.003030 | -4.03 | 4.58000 | 1396 | 1396 | bslmm | 0.001730 | 6.99e-02 | -5.305600 | 1.12e-07 | 0.017 | 0.004 | 0.603 | 0.126 | 0.250 | TRUE | -0.50 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 126605315 | 126605965 | PsychENCODE | Joint | No | 126605315-126605965 | 2.697747e-08 | FALSE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat | TNKS1BP1 | 11 | 0.17620 | rs11607056 | 4.970 | rs11228997 | 0.004394 | 4.05 | 3.09700 | 412 | 412 | blup | 0.092060 | 2.79e-05 | 4.922610 | 8.54e-07 | 0.080 | 0.025 | 0.107 | 0.032 | 0.756 | TRUE | 0.49 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 57067112 | 57092426 | GTEx Adrenal Gland | Joint | Yes | 57067112-57092426 | 6.695290e-07 | FALSE |
| Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat | AP000662.4 | 11 | 0.24480 | rs11607056 | 4.970 | rs11607122 | 0.095400 | -6.95 | 3.59200 | 396 | 46 | enet | 0.153544 | 2.82e-16 | -4.980256 | 6.35e-07 | 0.000 | 0.136 | 0.000 | 0.255 | 0.610 | FALSE | -0.62 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 57405497 | 57420263 | GTEx Thyroid | Marginal | Yes | 57405497-57420263 | 6.695290e-07 | FALSE |
| Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat | CLP1 | 11 | 0.04780 | rs11607056 | 4.970 | rs9420 | 0.035200 | 4.80 | 4.68900 | 390 | 4 | lasso | 0.032330 | 3.10e-04 | 5.195860 | 2.04e-07 | 0.001 | 0.008 | 0.002 | 0.015 | 0.974 | TRUE | 0.88 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 57424488 | 57429340 | GTEx Whole Blood | Joint | Yes | 57424488-57429340 | 6.695290e-07 | TRUE |
| PEC_TWAS_weights/ENSG00000134825.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000134825.wgt.RDat | TMEM258 | 11 | 0.07329 | rs174594 | 5.060 | rs174536 | 0.012300 | 5.95 | 4.36600 | 1206 | 9 | enet | 0.012700 | 2.35e-05 | 5.021730 | 5.12e-07 | 0.000 | 0.049 | 0.000 | 0.041 | 0.910 | TRUE | 0.73 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 61535973 | 61560274 | PsychENCODE | Joint | Yes | 61535973-61560274 | 4.192565e-07 | TRUE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat | DRD2 | 11 | 0.43370 | rs2514218 | -5.030 | rs4319541 | -0.004170 | -3.58 | 2.09000 | 526 | 526 | blup | 0.100150 | 2.99e-04 | -5.073787 | 3.90e-07 | 0.366 | 0.032 | 0.515 | 0.045 | 0.042 | TRUE | 0.00 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 113280318 | 113346111 | GTEx Frontal Cortex | Joint | Yes | 113280318-113346111 | 4.904798e-07 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat | OLFM4 | 13 | 0.03980 | rs12552 | 8.890 | rs1535576 | 0.027299 | -3.86 | -3.38200 | 507 | 507 | blup | 0.008642 | 2.69e-02 | 5.091290 | 3.56e-07 | 0.000 | 0.000 | 0.865 | 0.089 | 0.046 | TRUE | 0.42 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 53602875 | 53626196 | CMC DLPFC | Joint | No | 53602875-53626196 | 6.110891e-19 | FALSE |
| Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat | CTD-2298J14.2 | 14 | 0.11180 | rs1950829 | 5.940 | rs12431444 | 0.073500 | -5.91 | 5.69800 | 353 | 3 | lasso | 0.057336 | 8.03e-07 | -5.678860 | 1.36e-08 | 0.000 | 0.000 | 0.000 | 0.022 | 0.978 | TRUE | -0.87 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 42057064 | 42074059 | GTEx Thyroid | Joint | No | 42057064-42074059 | 2.850221e-09 | TRUE |
| Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat | Brain_Cerebellar_Hemisphere | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat | LRFN5 | 14 | 0.43200 | rs1950829 | 5.940 | rs11157247 | 0.204000 | 6.15 | 4.86000 | 353 | 353 | blup | 0.310050 | 1.13e-11 | 5.423400 | 5.85e-08 | 0.000 | 0.000 | 0.000 | 0.029 | 0.971 | FALSE | 0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere | 42076773 | 42373752 | GTEx Cerebellar Hemisphere | Marginal | No | 42076773-42373752 | 2.850221e-09 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat | LRFN5 | 14 | 0.41100 | rs1950829 | 5.940 | rs8008204 | 0.152180 | 5.94 | 5.09100 | 353 | 6 | lasso | 0.195060 | 6.59e-09 | 5.597540 | 2.17e-08 | 0.000 | 0.000 | 0.000 | 0.041 | 0.959 | FALSE | 0.89 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 42076773 | 42373752 | GTEx Cerebellum | Marginal | No | 42076773-42373752 | 2.850221e-09 | TRUE |
| Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat | JKAMP | 14 | 0.14070 | rs12893956 | 5.080 | rs1952039 | 0.037700 | -4.89 | 5.02000 | 460 | 460 | blup | 0.056384 | 9.88e-07 | -5.125100 | 2.97e-07 | 0.001 | 0.004 | 0.004 | 0.022 | 0.969 | FALSE | -0.62 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 59951161 | 59971429 | GTEx Thyroid | Marginal | Yes | 59951161-59971429 | 3.774349e-07 | TRUE |
| Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat | CCDC175 | 14 | 0.13690 | rs12893956 | 5.080 | rs2182140 | 0.076100 | -6.21 | 4.97200 | 424 | 424 | blup | 0.072917 | 2.66e-08 | -5.478850 | 4.28e-08 | 0.000 | 0.004 | 0.000 | 0.018 | 0.979 | TRUE | -0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 59971779 | 60043549 | GTEx Thyroid | Joint | Yes | 59971779-60043549 | 3.774349e-07 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat | RTN1 | 14 | 0.04170 | rs12893956 | 5.080 | rs12587247 | 0.025600 | -4.59 | 4.72000 | 420 | 420 | blup | 0.019700 | 1.78e-03 | -4.874920 | 1.09e-06 | 0.001 | 0.006 | 0.007 | 0.033 | 0.953 | FALSE | -0.85 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 60062693 | 60337557 | CMC DLPFC Splicing | Marginal | Yes | 60062693-60337557 | 3.774349e-07 | TRUE |
| Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat | RTN1 | 14 | 0.25130 | rs12893956 | 5.080 | rs1952039 | 0.154000 | -8.02 | 5.02000 | 358 | 6 | lasso | 0.152868 | 3.31e-16 | -5.348450 | 8.87e-08 | 0.000 | 0.003 | 0.000 | 0.016 | 0.981 | FALSE | -0.77 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 60062695 | 60337684 | GTEx Thyroid | Marginal | Yes | 60062695-60337684 | 3.774349e-07 | TRUE |
| NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat | NTR.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat | SYNE2 | 14 | 0.07960 | rs915057 | -6.150 | rs3020445 | 0.077706 | -10.10 | -5.71300 | 510 | 5 | lasso | 0.080663 | 9.92e-25 | 5.609528 | 2.03e-08 | 0.000 | 0.000 | 0.000 | 0.016 | 0.984 | FALSE | -0.94 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR | 64319682 | 64693151 | NTR Blood | Marginal | No | 64319682-64693151 | 7.748295e-10 | TRUE |
| Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat | Pituitary | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat | ESR2 | 14 | 0.21120 | rs915057 | -6.150 | rs1256033 | -0.005360 | 4.10 | -5.46600 | 456 | 19 | enet | 0.028550 | 1.97e-02 | -5.982300 | 2.20e-09 | 0.000 | 0.000 | 0.113 | 0.026 | 0.860 | TRUE | 0.70 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary | 64550950 | 64770377 | GTEx Pituitary | Joint | No | 64550950-64770377 | 7.748295e-10 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat | ESR2 | 14 | 0.10120 | rs915057 | -6.150 | rs6573553 | 0.050018 | 5.67 | -5.46200 | 457 | 9 | enet | 0.067100 | 2.81e-07 | -5.655371 | 1.56e-08 | 0.000 | 0.000 | 0.000 | 0.014 | 0.986 | FALSE | 0.91 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 64550950 | 64770377 | GTEx Whole Blood | Marginal | No | 64550950-64770377 | 7.748295e-10 | TRUE |
| PEC_TWAS_weights/ENSG00000119682.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000119682.wgt.RDat | AREL1 | 14 | 0.17261 | rs2005864 | 5.800 | rs11621186 | 0.138000 | 14.00 | -4.78500 | 1130 | 1130 | bslmm | 0.163000 | 0.00e+00 | -5.015110 | 5.30e-07 | 0.000 | 0.002 | 0.000 | 0.216 | 0.782 | FALSE | -0.77 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 75120140 | 75179818 | PsychENCODE | Marginal | No | 75120140-75179818 | 6.631492e-09 | FALSE |
| Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat | PROX2 | 14 | 0.06530 | rs1045430 | -5.710 | rs2300596 | 0.011200 | -4.49 | 5.58500 | 368 | 13 | enet | 0.011563 | 1.80e-02 | -5.758100 | 8.51e-09 | 0.000 | 0.000 | 0.017 | 0.020 | 0.962 | TRUE | -0.89 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 75319736 | 75330537 | GTEx Thyroid | Joint | No | 75319736-75330537 | 1.129762e-08 | TRUE |
| CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat | DLST | 14 | 0.09270 | rs1045430 | -5.710 | rs2111705 | 0.105642 | -7.28 | -4.40600 | 386 | 2 | lasso | 0.108580 | 3.96e-13 | 4.981400 | 6.31e-07 | 0.000 | 0.001 | 0.000 | 0.047 | 0.952 | FALSE | 0.89 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 75348593 | 75370450 | CMC DLPFC | Marginal | No | 75348593-75370450 | 1.129762e-08 | TRUE |
| PEC_TWAS_weights/ENSG00000119689.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000119689.wgt.RDat | DLST | 14 | 0.04731 | rs2005864 | 5.800 | rs8010840 | 0.024100 | 6.34 | 5.61000 | 1076 | 31 | enet | 0.032600 | 2.24e-11 | 5.089700 | 3.59e-07 | 0.000 | 0.000 | 0.000 | 0.023 | 0.977 | FALSE | 0.80 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 75348594 | 75370448 | PsychENCODE | Marginal | No | 75348594-75370448 | 6.631492e-09 | TRUE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat | RPS6KL1 | 14 | 0.08080 | rs1045430 | -5.710 | rs2359239 | 0.026700 | 4.40 | -4.17600 | 382 | 2 | lasso | 0.027820 | 2.48e-04 | -5.023810 | 5.07e-07 | 0.003 | 0.001 | 0.205 | 0.082 | 0.708 | FALSE | -0.57 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 75370656 | 75389188 | CMC DLPFC Splicing | Marginal | No | 75370656-75389188 | 1.129762e-08 | FALSE |
| PEC_TWAS_weights/ENSG00000198208.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000198208.wgt.RDat | RPS6KL1 | 14 | 0.01434 | rs2005864 | 5.800 | rs12888998 | -0.000719 | -3.58 | 5.27000 | 1056 | 1056 | bslmm | 0.001410 | 9.08e-02 | -4.952550 | 7.32e-07 | 0.002 | 0.000 | 0.176 | 0.031 | 0.791 | FALSE | -0.87 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 75370657 | 75390099 | PsychENCODE | Marginal | No | 75370657-75390099 | 6.631492e-09 | FALSE |
| PEC_TWAS_weights/ENSG00000244691.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000244691.wgt.RDat | RP11-600F24.2 | 14 | 0.01456 | rs10149470 | -5.930 | rs2403193 | 0.000357 | -3.98 | -5.16700 | 1598 | 2 | lasso | 0.003470 | 1.81e-02 | 5.185660 | 2.15e-07 | 0.007 | 0.002 | 0.552 | 0.202 | 0.238 | FALSE | -0.64 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 103878456 | 103879098 | PsychENCODE | Marginal | No | 103878456-103879098 | 3.029347e-09 | FALSE |
| YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat | CKB | 14 | 0.02530 | rs2296483 | -5.370 | rs10129426 | 0.023900 | -5.66 | -5.33600 | 349 | 4 | lasso | 0.018640 | 6.64e-07 | 5.346000 | 8.99e-08 | 0.000 | 0.001 | 0.000 | 0.005 | 0.995 | FALSE | -0.97 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 103985996 | 103989448 | YFS Blood | Marginal | Yes | 103985996-103989448 | 7.873664e-08 | TRUE |
| CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat | TRMT61A | 14 | 0.06010 | rs2296483 | -5.370 | rs942866 | 0.025997 | -4.79 | -5.21700 | 349 | 3 | lasso | 0.042040 | 6.61e-06 | 5.051300 | 4.39e-07 | 0.001 | 0.004 | 0.004 | 0.030 | 0.961 | FALSE | -0.57 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 103995508 | 104003410 | CMC DLPFC | Marginal | Yes | 103995508-104003410 | 7.873664e-08 | TRUE |
| Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat | Whole_Blood | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat | TRMT61A | 14 | 0.07160 | rs2296483 | -5.370 | rs7154572 | 0.017206 | -4.48 | -3.40800 | 354 | 3 | lasso | 0.034600 | 1.97e-04 | 4.977593 | 6.44e-07 | 0.006 | 0.010 | 0.049 | 0.081 | 0.854 | FALSE | -0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood | 103995521 | 104003410 | GTEx Whole Blood | Marginal | Yes | 103995521-104003410 | 7.873664e-08 | TRUE |
| Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat | RP11-894P9.2 | 14 | 0.08430 | rs2296483 | -5.370 | rs10129426 | 0.069700 | 5.42 | -5.33600 | 361 | 5 | lasso | 0.064092 | 1.84e-07 | -5.462560 | 4.69e-08 | 0.000 | 0.001 | 0.000 | 0.005 | 0.994 | TRUE | 0.94 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 104019758 | 104028214 | GTEx Thyroid | Joint | Yes | 104019758-104028214 | 7.873664e-08 | TRUE |
| PEC_TWAS_weights/ENSG00000270108.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000270108.wgt.RDat | RP11-73M18.6 | 14 | 0.03617 | rs10149470 | -5.930 | rs4906358 | 0.017500 | -5.52 | -4.62700 | 1758 | 1758 | bslmm | 0.017700 | 7.13e-07 | 5.031320 | 4.87e-07 | 0.000 | 0.005 | 0.001 | 0.413 | 0.581 | FALSE | -0.63 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 104153913 | 104154464 | PsychENCODE | Marginal | No | 104153913-104154464 | 3.029347e-09 | FALSE |
| PEC_TWAS_weights/ENSG00000269940.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000269940.wgt.RDat | RP11-73M18.7 | 14 | 0.06550 | rs10149470 | -5.930 | esv3635603 | 0.035600 | -8.21 | -4.55400 | 1755 | 13 | lasso | 0.055600 | 2.22e-18 | 4.856130 | 1.20e-06 | 0.000 | 0.006 | 0.000 | 0.513 | 0.480 | FALSE | -0.65 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 104160897 | 104161507 | PsychENCODE | Marginal | No | 104160897-104161507 | 3.029347e-09 | FALSE |
| Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat | Brain_Amygdala | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat | RP11-73M18.8 | 14 | 0.30000 | rs2296483 | -5.370 | rs2296483 | 0.181510 | -4.31 | -5.37200 | 353 | 5 | lasso | 0.111880 | 9.04e-04 | 5.142000 | 2.72e-07 | 0.010 | 0.002 | 0.082 | 0.019 | 0.887 | FALSE | -0.70 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala | 104162690 | 104163500 | GTEx Amygdala | Marginal | Yes | 104162690-104163500 | 7.873664e-08 | TRUE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat | AL049840.1 | 14 | 0.27100 | rs2296483 | -5.370 | rs861544 | 0.052800 | -4.75 | -4.50100 | 351 | 351 | blup | 0.082090 | 1.94e-04 | 5.029540 | 4.92e-07 | 0.001 | 0.003 | 0.008 | 0.026 | 0.962 | FALSE | -0.60 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 104177607 | 104179149 | GTEx Cerebellum | Marginal | Yes | 104177607-104179149 | 7.873664e-08 | TRUE |
| Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat | AL049840.1 | 14 | 0.26800 | rs2296483 | -5.370 | rs11625397 | 0.182495 | -5.06 | -5.13000 | 350 | 4 | lasso | 0.160000 | 8.82e-07 | 5.143620 | 2.69e-07 | 0.001 | 0.002 | 0.007 | 0.012 | 0.979 | FALSE | -0.72 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 104177607 | 104179149 | GTEx Cortex | Marginal | Yes | 104177607-104179149 | 7.873664e-08 | TRUE |
| Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat | Brain_Cortex | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat | RP11-73M18.9 | 14 | 0.24800 | rs2296483 | -5.370 | rs11625397 | 0.198292 | -5.37 | -5.13000 | 349 | 4 | lasso | 0.186000 | 1.06e-07 | 4.977330 | 6.45e-07 | 0.000 | 0.002 | 0.001 | 0.013 | 0.984 | FALSE | -0.69 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex | 104179904 | 104180441 | GTEx Cortex | Marginal | Yes | 104179904-104180441 | 7.873664e-08 | TRUE |
| PEC_TWAS_weights/ENSG00000269963.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000269963.wgt.RDat | RP11-73M18.9 | 14 | 0.01265 | rs10149470 | -5.930 | rs3759586 | 0.014300 | -4.83 | -4.84900 | 1749 | 2 | lasso | 0.010900 | 8.45e-05 | 4.830100 | 1.36e-06 | 0.001 | 0.005 | 0.049 | 0.425 | 0.520 | FALSE | -0.62 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 104179904 | 104180586 | PsychENCODE | Marginal | No | 104179904-104180586 | 3.029347e-09 | FALSE |
| PEC_TWAS_weights/ENSG00000118557.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000118557.wgt.RDat | PMFBP1 | 16 | 0.02226 | rs9936642 | 5.460 | rs8043722 | 0.000245 | -4.07 | 4.80200 | 1349 | 1349 | blup | 0.004720 | 7.14e-03 | -5.160620 | 2.46e-07 | 0.013 | 0.005 | 0.174 | 0.069 | 0.738 | TRUE | -0.70 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 72146056 | 72210777 | PsychENCODE | Joint | No | 72146056-72210777 | 4.761346e-08 | FALSE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat | MYO18A | 17 | 0.37380 | rs8066520 | 5.060 | rs4795491 | 0.123317 | -5.45 | 4.34000 | 337 | 337 | blup | 0.167285 | 1.26e-08 | -5.128570 | 2.92e-07 | 0.002 | 0.040 | 0.001 | 0.019 | 0.937 | FALSE | -0.62 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 27400528 | 27507430 | GTEx Adrenal Gland | Marginal | Yes | 27400528-27507430 | 4.192565e-07 | TRUE |
| Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat | Adrenal_Gland | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat | TIAF1 | 17 | 0.30370 | rs8066520 | 5.060 | rs869718 | 0.085762 | -5.08 | 3.64100 | 324 | 324 | blup | 0.095323 | 2.01e-05 | -5.361200 | 8.27e-08 | 0.016 | 0.111 | 0.008 | 0.055 | 0.810 | TRUE | -0.46 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland | 27401933 | 27405875 | GTEx Adrenal Gland | Joint | Yes | 27401933-27405875 | 4.192565e-07 | TRUE |
| PEC_TWAS_weights/ENSG00000264754.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000264754.wgt.RDat | CTD-2653B5.1 | 17 | 0.06754 | rs60856912 | 4.180 | rs8069128 | 0.025500 | -6.44 | -3.82146 | 1225 | 1225 | bslmm | 0.023300 | 1.44e-08 | 5.105730 | 3.30e-07 | 0.000 | 0.441 | 0.000 | 0.028 | 0.531 | TRUE | 0.13 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 65520597 | 65521538 | PsychENCODE | Joint | Yes | 65520597-65521538 | 2.915091e-05 | FALSE |
| PEC_TWAS_weights/ENSG00000041353.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000041353.wgt.RDat | RAB27B | 18 | 0.13240 | rs1262464 | -5.780 | rs12970424 | 0.057700 | -9.79 | -4.57400 | 1433 | 1433 | blup | 0.063700 | 7.52e-21 | 5.012900 | 5.36e-07 | 0.000 | 0.015 | 0.000 | 0.328 | 0.657 | TRUE | 0.11 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 52385091 | 52562747 | PsychENCODE | Joint | Yes | 52385091-52562747 | 7.470063e-09 | FALSE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat | RAB27B | 18 | 0.18080 | rs11875348 | -5.120 | rs2871673 | 0.173248 | -8.88 | -4.77300 | 368 | 9 | lasso | 0.159800 | 1.20e-18 | 4.843190 | 1.28e-06 | 0.000 | 0.016 | 0.000 | 0.038 | 0.945 | FALSE | 0.11 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 52495707 | 52562747 | CMC DLPFC Splicing | Marginal | Yes | 52495707-52562747 | 3.055357e-07 | TRUE |
| CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat | DDX27 | 20 | 0.04460 | rs11697370 | -4.640 | rs7266044 | 0.038362 | -5.08 | -3.95500 | 441 | 11 | enet | 0.069662 | 7.29e-09 | 4.836260 | 1.32e-06 | 0.003 | 0.067 | 0.001 | 0.029 | 0.900 | TRUE | -0.78 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 47835831 | 47860614 | CMC DLPFC | Joint | Yes | 47835831-47860614 | 3.484092e-06 | TRUE |
| Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat | Brain_Nucleus_accumbens_basal_ganglia | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat | SLC25A17 | 22 | 0.13620 | rs2179744 | 5.730 | rs13054099 | 0.098545 | 4.16 | 4.32200 | 287 | 8 | enet | 0.049730 | 6.36e-03 | 5.076990 | 3.83e-07 | 0.007 | 0.001 | 0.547 | 0.097 | 0.348 | FALSE | 0.74 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia | 41165634 | 41215403 | GTEx Nucleus accumbens | Marginal | No | 41165634-41215403 | 1.004306e-08 | FALSE |
| Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat | Thyroid | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat | SLC25A17 | 22 | 0.08390 | rs2179744 | 5.730 | rs5758064 | 0.044400 | -5.31 | -4.02000 | 289 | 289 | blup | 0.059630 | 4.87e-07 | 4.896100 | 9.78e-07 | 0.000 | 0.008 | 0.020 | 0.671 | 0.301 | FALSE | 0.47 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid | 41165634 | 41215403 | GTEx Thyroid | Marginal | No | 41165634-41215403 | 1.004306e-08 | FALSE |
| Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat | Brain_Frontal_Cortex_BA9 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat | XPNPEP3 | 22 | 0.22800 | rs2179744 | 5.730 | rs133076 | 0.006324 | 3.91 | 4.53000 | 282 | 15 | enet | 0.088290 | 6.68e-04 | 4.951000 | 7.38e-07 | 0.009 | 0.001 | 0.731 | 0.091 | 0.168 | FALSE | 0.45 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 | 41253088 | 41351450 | GTEx Frontal Cortex | Marginal | No | 41253088-41351450 | 1.004306e-08 | FALSE |
| CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat | CMC.BRAIN.RNASEQ | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat | XPNPEP3 | 22 | 0.05530 | rs2179744 | 5.730 | rs138354 | 0.035200 | -5.57 | -4.32500 | 318 | 4 | lasso | 0.044230 | 3.86e-06 | 5.110000 | 3.21e-07 | 0.000 | 0.004 | 0.005 | 0.358 | 0.632 | FALSE | 0.64 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ | 41258260 | 41363888 | CMC DLPFC | Marginal | No | 41258260-41363888 | 1.004306e-08 | FALSE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat | EP300 | 22 | 0.11400 | rs2179744 | 5.730 | rs3171692 | 0.040045 | 4.45 | 4.69400 | 276 | 276 | blup | 0.063473 | 9.81e-04 | 5.493900 | 3.93e-08 | 0.001 | 0.001 | 0.061 | 0.049 | 0.888 | FALSE | 0.85 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 41487790 | 41576081 | GTEx Cerebellum | Marginal | No | 41487790-41576081 | 1.004306e-08 | TRUE |
| YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat | YFS.BLOOD.RNAARR | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat | EP300 | 22 | 0.04512 | rs2179744 | 5.730 | rs139480 | 0.042424 | -7.51 | -4.00200 | 284 | 284 | blup | 0.046630 | 5.18e-15 | 5.059100 | 4.21e-07 | 0.000 | 0.012 | 0.000 | 0.955 | 0.033 | FALSE | 0.61 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR | 41487790 | 41576081 | YFS Blood | Marginal | No | 41487790-41576081 | 1.004306e-08 | FALSE |
| CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat | CMC.BRAIN.RNASEQ_SPLICING | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat | RANGAP1 | 22 | 0.02850 | rs2179744 | 5.730 | rs5758209 | -0.002214 | -3.04 | -4.88500 | 251 | 251 | blup | 0.003925 | 9.85e-02 | 5.240100 | 1.61e-07 | 0.010 | 0.000 | 0.814 | 0.028 | 0.147 | FALSE | 0.57 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING | 41641614 | 41682216 | CMC DLPFC Splicing | Marginal | No | 41641614-41682216 | 1.004306e-08 | FALSE |
| PEC_TWAS_weights/ENSG00000100401.wgt.RDat | PsychENCODE | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000100401.wgt.RDat | RANGAP1 | 22 | 0.05133 | rs5758265 | 5.780 | rs5751074 | 0.041600 | -7.48 | 4.72400 | 1064 | 4 | lasso | 0.053336 | 1.13e-17 | -5.575273 | 2.47e-08 | 0.000 | 0.004 | 0.000 | 0.705 | 0.290 | FALSE | -0.81 | /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights | 41641615 | 41682255 | PsychENCODE | Marginal | No | 41641615-41682255 | 7.470063e-09 | FALSE |
| Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat | Brain_Cerebellum | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat | ZC3H7B | 22 | 0.11000 | rs2179744 | 5.730 | rs6002271 | 0.072645 | 4.09 | 5.58700 | 269 | 4 | lasso | 0.037831 | 9.13e-03 | 5.729100 | 1.01e-08 | 0.001 | 0.000 | 0.105 | 0.031 | 0.862 | TRUE | 0.93 | /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum | 41697526 | 41756151 | GTEx Cerebellum | Joint | No | 41697526-41756151 | 1.004306e-08 | TRUE |
3.7 FOCUS
Merge the relevent FOCUS databases
# Create a list of databases to be merged.
cat << 'EOF' > /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Adrenal_Gland.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Amygdala.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Anterior_cingulate_cortex_BA24.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Caudate_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellar_Hemisphere.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellum.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cortex.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Frontal_Cortex_BA9.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hippocampus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hypothalamus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Putamen_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Spinal_cord_cervical_c-1.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Substantia_nigra.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/NTR.BLOOD.RNAARR.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/PEC_TWAS_weights.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Pituitary.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Thyroid.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Whole_Blood.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/YFS.BLOOD.RNAARR.db
EOF
####
# Merge the databases
####
# I have written a script to do this in R
/users/k1806347/brc_scratch/Software/Rscript.sh /scratch/users/k1806347/Software/MyGit/FOCUS_db_merger/FOCUS_db_merger.r \
--dbs /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt \
--out /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS
# This approach is giving strange results.
# Delete the outputCreate FOCUS database for MDD TWAS
# Merging the databases causes some strange warnings
# Use the standard approach for the time being
########
# Import the FUSION SNP-weights to FOCUS format
########
# Import with all tissues in the MDD TWAS combined
# A shell script to do this has been written
mkdir -p /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db
# Fusion weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_fusion.sh
# Onyl continue once the FUSION SNP-weights are in the database
# Psych ENCODE weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_psychENCODE.shRun FOCUS
########
# Finemap TWAS associations
########
# Use a threshold that will run FOCUS for all TWAS significant loci (max GWAS.P = 5e-6)
for chr in $(seq 22 22); do
sbatch -p brc,shared --mem=10G /users/k1806347/brc_scratch/Software/focus.sh finemap /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr${chr} /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS.db --chr ${chr} --p-threshold 5e-6 --plot --out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD.FOCUS.MDD_TWAS_db.chr${chr}
done
Process the FOCUS results
library(data.table)
fusion <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
focus.files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/', pattern=glob2rx("MDD.FOCUS.MDD_TWAS_db.chr*.focus.tsv"))
length(focus.files)
focus<-NULL
for(i in focus.files){
focus<-rbind(focus,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/',i)))
}
# I noticed a bug in the output where features that should be in the 90% credible set are not
focus_bug<-NULL
for(i in unique(focus$region)){
focus_temp<-focus[focus$region == i,]
if(sum(focus_temp$in_cred_set) == 0 & max(focus_temp$pip) != focus_temp$pip[focus_temp$ens_gene_id == 'NULL.MODEL']){
print(head(focus_temp))
focus_bug<-rbind(focus_bug, focus_temp)
}
}
# This shows several features should be in the credible set
focus$in_cred_set[focus$ens_gene_id == 'DENND1B' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '1:197311514-1:199239815'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'OLFM4' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '13:53339645-13:54682393'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'ENSG00000229267' & focus$tissue == 'pec_twas_weights' & focus$region == '2:214014511-2:215573795'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'COQ3' & focus$tissue == 'cmc.brain.rnaseq_splicing' & focus$region == '6:97842747-6:100629728'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'OSBPL3' & focus$tissue == 'pituitary' & focus$region == '7:23471523-7:25077097'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'PXDNL' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '8:50082470-8:53302930'] <- 1
# Update tissue for psychencode features
focus_psychencode<-focus[focus$tissue == 'pec_twas_weights',]
focus_fusion<-focus[focus$tissue != 'pec_twas_weights',]
focus_psychencode$tissue<-'psychencode'
# Update PsychENCODE gene IDs from ensembl to gene names
library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)
focus_psychencode<-merge(focus_psychencode, Genes, by.x='mol_name', by.y='ensembl_gene_id')
focus_psychencode$mol_name<-focus_psychencode$external_gene_name
focus_psychencode$external_gene_name<-NULL
focus_psychencode<-focus_psychencode[,names(focus),with=F]
focus<-rbind(focus_fusion,focus_psychencode)
fusion$tissue<-tolower(fusion$PANEL)
fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('tissue','ID'), by.y=c('tissue','mol_name'), all.x=T)
fusion_focus<-fusion_focus[,c('CHR','P0','P1','PANEL_clean_short','ID','TWAS.Z','TWAS.P','twas_z','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)
fusion_focus<-fusion_focus[,c('Location','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region'),with=F]
write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)Combine FOCUS results with novelty table
library(data.table)
focus<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv')
fusion$tissue<-tolower(fusion$PANEL)
fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('tissue','ID'), by.y=c('tissue','mol_name'), all.x=T)
fusion_focus<-fusion_focus[,c('WGT','CHR','P0','P1','PANEL_clean','ID','TWAS.Z','TWAS.P','Novel','Colocalised','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('WGT','CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','Novel','Colocalised','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)
# Remove the MHC region
fusion_focus_noMHC<-fusion_focus[!(fusion_focus$CHR == 6 & fusion_focus$P1 > 26e6 & fusion_focus$P0 < 34e6),]
# Subset those which are high confidence
fusion_focus_highConf<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 3.685926e-08,]
# One high confidence gene is novel (TMEM106B)
# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$TWAS.P < 1.368572e-06,]
sum(duplicated(fusion_focus_TWsig$WGT))
fusion_focus_TWsig[duplicated(fusion_focus_TWsig$WGT),]
# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 1.368572e-06,]
write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)Show FOCUS results
Show FOCUS and other combined results
3.8 TWAS-GSEA
Combine the predicted expression files for FUSION and PsychENCODE
library(data.table)
FUSION<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Predicted_expression/FUSION_1KG/FUSION_1KG_Expr_AllSets.csv.gz')
PsychENCODE<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/Predicted_expression/FeaturePredictions.csv.gz')
both<-merge(FUSION, PsychENCODE, by=c('FID','IID'))
fwrite(both, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv', row.names=F, quote=F)gzip /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csvTWAS-GSEA: All tissues
# Using TWAS from all PANELs, removing duplicate genes.
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--n_cores 3 \
--self_contained F \
--min_r2 0.05 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray
# hypothesis-free analysis
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--self_contained F \
--min_r2 0.05 \
--n_cores 3 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free
# brainspan
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--self_contained F \
--min_r2 0.05 \
--linear_p_thresh 1 \
--n_cores 3 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_brainspan
TWAS-GSEA: Tissue groups
library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')
Brain_res<-res[grepl('Brain|BRAIN|PsychENCODE', res$PANEL),]
HPA_res<-res[grepl('Adrenal|Pituitary|Hypothalamus', res$PANEL),]
HPT_res<-res[grepl('Thyroid|Pituitary|Hypothalamus', res$PANEL),]
BLOOD_res<-res[grepl('BLOOD', res$PANEL),]
write.table(Brain_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BRAIN.GW', row.names=F, col.names=T, quote=F)
write.table(HPA_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPA.GW', row.names=F, col.names=T, quote=F)
write.table(HPT_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPT.GW', row.names=F, col.names=T, quote=F)
write.table(BLOOD_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BLOOD.GW', row.names=F, col.names=T, quote=F)# Candidate
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--n_cores 1 \
--self_contained F \
--min_r2 0.05 \
--competitive T \
--linear_p_thresh 1 \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Candidate_Wray
done
# Hypo-free
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--n_cores 1 \
--self_contained F \
--min_r2 0.05 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Hypo_free
done
# brainspan
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--self_contained F \
--min_r2 0.05 \
--n_cores 3 \
--linear_p_thresh 1 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_brainspan
done
TWAS-GSEA: Tissue-specific
library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')
for(i in unique(res$PANEL)){
write.table(res[res$PANEL == i,], paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_',i,'.GW'), row.names=F, col.names=T, quote=F)
}for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--n_cores 1 \
--self_contained F \
--linear_p_thresh 1 \
--competitive T \
--min_r2 0.05 \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Candidate_Wray
done
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--self_contained F \
--competitive T \
--min_r2 0.05 \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Hypo_free
done
# brainspan
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do
sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
--twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
--pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
--prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
--qqplot F \
--use_alt_id ID \
--expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
--self_contained F \
--min_r2 0.05 \
--linear_p_thresh 1 \
--n_cores 3 \
--competitive T \
--covar GeneLength,NSNP \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_brainspan
done
# Note. The individual tissue analyses return more significant findings. Instead of aggregating tissues, perhaps meta-analysis of per tissue results would be more effective.Tabulate the results
library(data.table)
#####
# AllTissue results
#####
##
# Candidate
##
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray.competitive.txt')
res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
PMID=gsub('.*\\.','',res$GeneSet),
res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
write.csv(res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/AllTissue_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)
##
# Hpothesis free
##
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free.competitive.txt')
res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
PMID=gsub('.*\\.','',res$GeneSet),
res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
####
# Tissue-set analysis
####
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')
res_files<-res_files[grepl('Candidate', res_files)]
tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}
tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL
write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/TissueSet_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')
res_files<-res_files[grepl('Hypo_free', res_files)]
tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}
tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL
####
# Tissue specific analyses
####
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')
res_files<-res_files[grepl('Candidate', res_files)]
tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}
tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL
write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')
res_files<-res_files[grepl('Hypo_free', res_files)]
tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}
tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL
write.csv(tissue_hypo_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Hypo_free_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)
############
# Table and make figures for brainspan enrichment analysis
############
##
# All tissue
##
library(ggplot2)
library(stringr)
library(cowplot)
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA', pattern='competitive.txt')
res_files<-res_files[grepl('brainspan', res_files)]
res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/',i))
res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))
res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)
res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'
res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
geom_bar(stat="identity", position=position_dodge()) +
theme_half_open() +
background_grid() +
scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.position = 'none') +
ggtitle('All Tissues') +
geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}
png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_AllTissue_BrainSpan.png', units='px', res=300, width=1500, height=1000)
plot_grid(plotlist=res_plot, ncol=1)
dev.off()
##
# Tissue set
##
library(ggplot2)
library(stringr)
library(cowplot)
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')
res_files<-res_files[grepl('brainspan', res_files)]
res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))
res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)
res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'
res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
geom_bar(stat="identity", position=position_dodge()) +
theme_half_open() +
background_grid() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.position = 'none') +
ggtitle(gsub('_brainspan.competitive.txt','',i)) +
geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}
png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/MDD_TWAS_ByPanelSet_BrainSpan.png', units='px', res=300, width=2500, height=1500)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()
##
# Tissue-specific
##
library(ggplot2)
library(stringr)
library(cowplot)
res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')
res_files<-res_files[grepl('brainspan', res_files)]
res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))
res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)
res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'
res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
geom_bar(stat="identity", position=position_dodge()) +
theme_half_open() +
background_grid() +
scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.position = 'none') +
ggtitle(gsub('_brainspan.competitive.txt','',i)) +
geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}
png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/MDD_TWAS_ByPanel_BrainSpan.png', units='px', res=300, width=3000, height=7000)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()Show candidate TWAS-GSEA results
| GeneSet | PMID | Estimate | SE | T | N_Mem_Avail | N_Mem | P | P.CORR |
|---|---|---|---|---|---|---|---|---|
| RBFOX2 | 24613350 | 0.08092875 | 0.02482267 | 3.260276 | 2445 | 3031 | 0.0005565194 | 0.03951288 |
| GeneSet | PMID | Estimate | SE | T | N_Mem_Avail | N_Mem | P | P.CORR | Tissue |
|---|---|---|---|---|---|---|---|---|---|
| RBFOX2 | 24613350 | 0.10697919 | 0.02587590 | 4.134318 | 2373 | 3031 | 1.780048e-05 | 0.001263834 | BRAIN |
| SCZ.COMPOSITE | 24463508 | 0.11324126 | 0.03114801 | 3.635586 | 1343 | 1787 | 1.386748e-04 | 0.004922957 | BRAIN |
| RBFOX1.RBFOX3 | 24613350 | 0.08366670 | 0.02424599 | 3.450744 | 2627 | 3400 | 2.795220e-04 | 0.006615355 | BRAIN |
| FMRP | 21784246 | 0.12461728 | 0.03715019 | 3.354419 | 937 | 1240 | 3.976596e-04 | 0.007058457 | BRAIN |
| POTENTIALLY.SYNAPTIC.ALL | 27694994 | 0.06031635 | 0.01988561 | 3.033166 | 4384 | 5736 | 1.210011e-03 | 0.017182151 | BRAIN |
| PGC.BP.P10.4 | 21926972 | 0.18130846 | 0.06366349 | 2.847919 | 324 | 629 | 2.200306e-03 | 0.026036954 | BRAIN |
| NEURONAL.PSD | 23071613 | 0.08920852 | 0.03345738 | 2.666333 | 1131 | 1444 | 3.834183e-03 | 0.038889571 | BRAIN |
| GeneSet | PMID | Estimate | SE | T | N_Mem_Avail | N_Mem | P | P.CORR | Tissue |
|---|---|---|---|---|---|---|---|---|---|
| MIR.137 | 24463508 | 0.3550824 | 0.09912228 | 3.582266 | 130 | 421 | 0.0001703133 | 0.01021880 | CMC.BRAIN.RNASEQ |
| SCZ.DENOVO.NONSYN | 24463508 | 0.4039377 | 0.11510465 | 3.509308 | 83 | 604 | 0.0002246369 | 0.01168112 | Pituitary |
| SCZ.COMPOSITE | 24463508 | 0.2323825 | 0.07087671 | 3.278687 | 233 | 1787 | 0.0005214569 | 0.01355788 | Pituitary |
| SCZ.COMPOSITE | 24463508 | 0.2677617 | 0.08385283 | 3.193234 | 188 | 1787 | 0.0007034446 | 0.03235845 | Brain_Caudate_basal_ganglia |
| CONSTRAINED | 25086666 | 0.2667025 | 0.08985060 | 2.968289 | 157 | 1003 | 0.0014973135 | 0.03436180 | CMC.BRAIN.RNASEQ |
| RBFOX1.RBFOX3 | 24613350 | 0.1219388 | 0.04167769 | 2.925758 | 938 | 3400 | 0.0017180898 | 0.03436180 | CMC.BRAIN.RNASEQ |
| PGC.SCZ.P10.4 | 24463508 | 0.2749434 | 0.10082558 | 2.726921 | 155 | 442 | 0.0031964205 | 0.04794631 | CMC.BRAIN.RNASEQ |
Show hypothesis-free TWAS-GSEA results
| GeneSet | Estimate | SE | T | N_Mem_Avail | N_Mem | P | P.CORR | Tissue |
|---|---|---|---|---|---|---|---|---|
| GO.MACROMOLECULAR.COMPLEX.BINDING | 0.4625439 | 0.09122296 | 5.070477 | 152 | 1365 | 1.984100e-07 | 0.000598603 | Brain_Caudate_basal_ganglia |
| GO.MICROTUBULE.BINDING | 1.0368947 | 0.22172666 | 4.676455 | 25 | 191 | 1.459384e-06 | 0.002201480 | Brain_Caudate_basal_ganglia |
| GO.ALCOHOL.BINDING | 1.7881848 | 0.38352213 | 4.662534 | 8 | 99 | 1.561701e-06 | 0.005161421 | Pituitary |
| GO.CHROMATIN.BINDING | 0.7897970 | 0.18867269 | 4.186070 | 35 | 422 | 1.419132e-05 | 0.014077837 | Brain_Caudate_basal_ganglia |
| GO.PROTEIN.COMPLEX.BINDING | 0.4429150 | 0.10741456 | 4.123417 | 110 | 913 | 1.866468e-05 | 0.014077837 | Brain_Caudate_basal_ganglia |
| GO.LIGAND.DEPENDENT.NUCLEAR.RECEPTOR.BINDING | 1.9756846 | 0.49658009 | 3.978582 | 5 | 22 | 3.466376e-05 | 0.020916114 | Brain_Caudate_basal_ganglia |
| GO.REGULATION.OF.INTRINSIC.APOPTOTIC.SIGNALING.PATHWAY | 1.7918397 | 0.43272770 | 4.140802 | 6 | 138 | 1.730469e-05 | 0.032982733 | Brain_Amygdala |
Show developmental stage enrichment plots
MDD TWAS: All Tissues BrainSpan
MDD TWAS: Tissue-sets BrainSpan
***
4 Comparison with previous TWAS literature
I need Lorenza to send me the results files whe prepared previously for this comparison. Alternatively, Lorenza could run this section. The below code has not been run or edited to work with new files yet.
4.1 Wray et al. (2018) and Gaspar et al. (2019)
Show code
########################################
# Comparing our results to previous TWASs
###############################################
# our TWAS, Wray et al TWAS, Gaspar et al. TWAS
rm(list=ls())
###
# Load data
###
library(data.table)
#these are all files with significant features only. NB the Gaspar file includes findings from all snp weights from any
#gene sign. in at least one tissue
our_hits <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/AllTissues_CLEAN.txt")
our_hits_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/signtest.AllTissues_CLEAN.txt")
Gaspar_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Gaspar et al. hits.txt")
Wray_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Wray et al. hits.csv")
###
# Structure df so that they are similar
###
#1. GASPAR (NB this includes info on TWAS.Z values not p values!!!)
#melt the gaspar hits df so that it presents one column with all SNP-weigths and one with the values
Gaspar_hits2 <- melt(Gaspar_hits, id = c("target", "CHROMOSOME"))
head(Gaspar_hits2)
#rename cols
Gaspar_hits2$PANEL <- Gaspar_hits2$variable
Gaspar_hits2$TWAS.Z <- Gaspar_hits2$value
Gaspar_hits2$variable <- NULL
Gaspar_hits2$value <- NULL
#2. Wray et al
#keep panel, ID, and TWAS.P & TWAS.Z info only
library(tidyverse)
Wray_hits <- as_tibble(Wray_hits)
Wray_hits
Wray_hits_filt <- Wray_hits %>% select("Gene", "CHR", "TWAS.Z", "TWAS.P")
#ALL
#rename columns so they all correspond
Gaspar_hits2$ID <- Gaspar_hits2$target
Gaspar_hits2$CHR <- Gaspar_hits2$CHROMOSOME
str(Gaspar_hits2)
Gaspar_hits2$target <- NULL
Gaspar_hits2$CHROMOSOME <- NULL
Wray_hits_filt$ID <- Wray_hits_filt$Gene
Wray_hits_filt$Gene <- NULL
#filter our findings for the cols needed only
our_hits <- as_tibble(our_hits)
colnames(our_hits)
our_hits_filt <- our_hits %>% select("PANEL", "PANEL_clean_short", "ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P")
###
# Fix SNP-weights
###
#No need for this
#Wray_hits_filt$PANEL_Wray <- "CMC DLPFC"
#our_hits_filt$PANEL_DallAglio <- our_hits_filt$PANEL_clean_short
#Gaspar_hits2$PANEL_Gaspar <- Gaspar_hits2$PANEL_Gaspar
###
# Filter our findings for any gene significant in either study
###
sign_genes_our <- our_hits_sign$ID
sign_genes_Wray <- Wray_hits_filt$ID
sign_genes_Gaspar <- Gaspar_hits2$ID
Gaspar <- unique(sign_genes_Gaspar) #25 unique genes
Wray <- unique(sign_genes_Wray) #17 unique genes
Us <- unique(sign_genes_our) #91 unique genes
#create a general variable with all sign. genes in any paper
All <- c(Gaspar, Wray, Us)
All #133 features
#keep only unique gene IDs
All_unique <- unique(All)
All_unique #106 genes
###
#Keep only genes sign, in either of the three studies
###
#filter by genes which are in the vector containing sign. features in any of the studies
our_hits_final <- our_hits_filt[(our_hits_filt$ID %in% All_unique), ] #629 observations
#keep only unique genes in our study (those with the greatest absolute z-score)
our_hits_final<-our_hits_final[!is.na(our_hits_final$TWAS.Z),]
our_hits_final2 <- our_hits_final[order(abs(our_hits_final$TWAS.Z), decreasing = T), ]
library(dplyr)
our_hits_final3 <- our_hits_final2 %>% distinct(ID, .keep_all = T) #102 obs.
our_hits_correct <- our_hits_final3
#keep only unique genes in the Gaspar et al study
Gaspar_hits2b<-Gaspar_hits2[!is.na(Gaspar_hits2$TWAS.Z),]
Gaspar_hits3 <- Gaspar_hits2b[order(abs(Gaspar_hits2b$TWAS.Z), decreasing = T), ]
library(dplyr)
Gaspar_hits4 <- Gaspar_hits3 %>% distinct(ID, .keep_all = T) #25 gene IDs, as expected
Gaspar_correct <- Gaspar_hits4
#NB no need to keep only unique genes for the Wray et al TWAS as that is tested in one tissue only
#exclude missings in the Wray et al
Wray_hits_correct <- Wray_hits_filt[!is.na(Wray_hits_filt$TWAS.Z), ]
Wray_hits_correct
###
# Change the names of variables which have the same col name but distinct values across the three df
###
Gaspar_correct$Gaspar_TWAS.Z <- Gaspar_correct$TWAS.Z
Gaspar_correct$TWAS.Z <- NULL
Wray_hits_correct$Wray_TWAS.Z <- Wray_hits_correct$TWAS.Z
Wray_hits_correct$TWAS.Z <- NULL
Wray_hits_correct$Wray_TWAS.P <- Wray_hits_correct$TWAS.P
Wray_hits_correct$TWAS.P <- NULL
our_hits_correct$DallAglio_TWAS.Z <- our_hits_correct$TWAS.Z
our_hits_correct$DallAglio_TWAS.P <- our_hits_correct$TWAS.P
our_hits_correct$TWAS.Z <- NULL
our_hits_correct$TWAS.P <- NULL
our_hits_correct$PANEL <- NULL
our_hits_correct$PANEL_clean_short <- NULL
Wray_hits_correct$CHR <- NULL
Wray_hits_correct$Wray_TWAS.P <- NULL
Wray_hits_correct$TWAS.P <- NULL
Gaspar_correct$CHR <- NULL
Gaspar_correct$PANEL <- NULL
###
#join your hits with the hits from the wray et al. paper and Gaspar et al paper
###
#since the three tibbles do not have the same nrow, we cannot use merge. But we can use full_join
Gaspar_correct <- as_tibble(Gaspar_correct) #transform all df into tibbles to use full join. the other two
##df are already tibbles. this was the only one left to convert
#join hits bw Gaspar and our study
table_correct<- full_join(our_hits_correct, Gaspar_correct) #dim 106, 7
#merge the newly created table with the wray et al findings too
table_correct2 <- full_join(table_correct, Wray_hits_correct) #dim 106, 8
#check that no gene is repeated and that z-scores are present for the three studies (should be high z-scores)
table_correct2 <- table_correct2[order(table_correct2$ID), ]
table_correct2 #everything looks fine
#delete the Dall'Aglio TWAS.P col
table_correct2$DallAglio_TWAS.P <- NULL
#add a column saying whether the gene was transcriptome-wide sign. in our study
table_correct2$Transcriptome_wide_sign_DallAglio <- ifelse(table_correct2$DallAglio_TWAS.Z > 4.74 | table_correct2$DallAglio_TWAS.Z < -4.74, "Sign.", "Not Sign.")
###
# Clean up and save
###
my_data <- as_data_frame(table_correct2)
colnames(my_data)
col_order <- c("ID", "Gaspar_TWAS.Z", "Wray_TWAS.Z", "DallAglio_TWAS.Z", "Transcriptome_wide_sign_DallAglio")
my_data2 <- my_data[, col_order]
my_data2
#save as csv file (each df col is treated as independent col, +excel format)
write.csv(my_data2, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/comparison_previousTWASs.csv', row.names=F)
my_data2Findings When comparing the Wray et al. (2018) results from the CMC DLPFC with our findings, we can observe that we obtained identical results in terms of TWAS z-values. This is true also for two genes which do not show concordance in effect size: SLC25A17 and DENND1B. This is due to us findings stronger associations in those genes in panels which were not tested by the Wray et al study (i.e. the nucleus accumbens and the CMC DLPFC splicing). Generally, Wray et al. (2018) identified a greater number of associations, but this was due to the lower multiple testing burden they presented as opposed to our study which tested 20 SNP-weight sets instead of one.
When comparing the Gaspar et al. (2019) results with our findings, it can be noted that results were generally very similar. Inconsistencies were present in four genes (DENND1B, KLC1, ZMYND8, ZNF165), where different directions of effect were identified. This is due to (i) the association being the strongest in our study within the CMC DLPFC splicing panel (where inconsistent direction of effects are generally present), (ii) our finding being from a SNP-weight panel not tested in the Gaspar et al. study (e.g. KLC1 z-score = -4.7 for the thyroid and ZNF165 z-score = 4.18 for the thyroid tissue), (iii) the Gaspar et al. study testing SNP-weight panels we did not test (e.g. for ZMYND8, a high z-score was found by them in the DGN whole blood panel). Interestingly, four associations within genes which were not tested in our study due to their low heritability in the tested SNP-weights were instead identified by the Gaspar study, namely BTN1A1, HIST1H2AK, TMEM33, TMX2.These were the strongest and significant in the DGN blood weight set (for BTN1A1, HIST1H2AK and TMX2), and in the GTEx cerebellum SNPweight set for TMEM33. The latter is of high interest since the same SNP-weight was tested in our study, but it could not surpass the heritability threshold for feature selection.
5 Comparison with the observed gene expression TWAS of depression
5.1 Our finding compared to those from Jansen et al. (2016)
Show code
##################
# Comparing our findings to the findings from Jansen et al. (TWAS of observed gene expression)
##################
###
# Load data
###
rm(list=ls())
library(data.table)
library(dplyr)
our_hits <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/signtest.AllTissues_CLEAN.txt")
Jansen_alloutput <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Jansen et al. hits.csv")
our_alloutput <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/AllTissues_CLEAN.txt")
#filter for significant features only in the Jansen paper
colnames(Jansen_alloutput)
Jansen_sign <- filter(Jansen_alloutput, Jansen_alloutput$`FDR control vs current`< 0.1) #this is for the Jansenvsour study comparison
#keep only needed columns in both df
library(tidyverse)
our_hits <- as_tibble(our_hits)
our_hits
our_hits_filt <- our_hits %>% select("ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P", "PANEL_clean_short")
###
#Comparing our results vs Jansen et al's
###
our_hits[order(our_hits$ID), ]
Jansen_alloutput[order(Jansen_alloutput$Gene), ]
table_ourvsJansen2 <- merge(our_hits, Jansen_alloutput, by.x = "ID", by.y = "Gene")
validated <- table_ourvsJansen2[(table_ourvsJansen2$`P control vs current`< 0.05), ]
#42 features which were significant in our study, were also nominally significant in their study (for either one of the three comparison types)
unique(validated$ID)
#these 42 hits corresponded to 14 unique genes
#[1] "ANKRD44" "CKB" "COQ3" "DLST" "EP300" "FLOT1" "OSBPL3" "PCDHA8" "RAB27B"
#[10] "RERE" "SYNE2" "TMEM106B" "TRMT61A" "ZSCAN16"
nrow(validated) #42 rows
###
#create a table comparing our findings to theirs
###
#Order cols as you like
library(tibble)
validated <- as_data_frame(validated)
colnames(validated)
#order the table by CHR and then P0
str(validated)
validated$CHR <- as.numeric(as.character(validated$CHR))
validated$P0 <- as.numeric(as.character(validated$P0))
validated <- validated[order(validated$CHR, validated$P0), ]
#fix columns
validated$Location <- paste0('chr',validated$CHR,':', validated$P0,'-',validated$P1)
validated$DallAglio_Zscore <- validated$TWAS.Z
validated$DallAglio_pvalue <- validated$TWAS.P
validated$Jansen_pvalue_controlsvscurrent <- validated$`P control vs current`
validated$Jansen_zscore_controlsvscurrent <- validated$`B control vs current`
#set a column order
col_order <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")
validated <- validated[, col_order]
validated
#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies
#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 14 #Bonf. significance = nominal p value / number of unique genes
# = 0.003571429
validated$Bonf_validated <- ifelse(validated$Jansen_pvalue_controlsvscurrent < 0.003571429, "Yes", "No")
#2) add the column specifying whether direction of effects is consistent
validated$Consistent_dir_effect <- ifelse((validated$Jansen_zscore_controlsvscurrent > 0 & validated$DallAglio_Zscore > 0) | (validated$Jansen_zscore_controlsvscurrent < 0 & validated$DallAglio_Zscore <0), "Yes", "No")
sum(validated$Consistent_dir_effect == "Yes") #17 associations present the same direction of effect
sum(validated$Consistent_dir_effect == "No") #25 associations present a different direction of effect
sum(validated$Bonf_validated == "Yes") #12 associations Bonferroni validated
print(validated$ID[validated$Bonf_validated == "Yes"]) # "RERE" "RERE" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B"
#"TMEM106B" "EP300" "EP300"
#these 12 validated associations come from 3 unique genes = RERE, TMEM106B, EP300
#change col order again
col_order_2 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")
validated <- validated[, col_order_2]
head(validated)
#save as csv file (each df col is treated as independent col, +excel format)
write.csv(validated, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/OurFindingsvsJansen_correct.csv', row.names = F)
validatedFindings When comparing our findings to those from Jansen et al. (2016) it can be observed that their results could validate 42 of our associations at a nominal p-value level and 12 at a Bonferroni significance level. Of these associations, 14 and 3 unique genes could be validated, respectively. The three Bonferroni validated genes include RERE, TMEM106B, EP300. However, for RERE, TMEM106B, inconsistent direction of effect was generally present. Contrarily, EP300 was consistently upregulated in both studies.
5.1.1 Jansen et al. (2016) findings compared to ours
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####
# Comparing the Jansen et al results to ours
####
#this script is the continuation of the one before
#order both df by gene ID
our_alloutput[order(our_alloutput$ID), ]
Jansen_sign[order(Jansen_sign$Gene), ]
#merge
table_Jansenvsours <- merge(our_alloutput, Jansen_sign, by.x = "ID", by.y = "Gene")
Replicated_byus <- table_Jansenvsours[(table_Jansenvsours$TWAS.P < 0.05), ]
#54 observations
unique(Replicated_byus$ID)
#pertain to 28 IDs: [1] "AMICA1" "ARHGEF7" "ARL4C" "ASPH" "CCDC116" "CD47" "COA1" "CPEB4" "DDHD1" "DENND4C" "FBXO3" "GNPTAB"
#[13] "GOT2" "IL6R" "INVS" "KTN1" "MBNL1" "MEFV" "MTSS1" "MYH9" "NAPG" "NCALD" "NUPL2" "OSTM1"
#[25] "PAPPA2" "SP4" "TMEM136" "TMEM64"
###
#FIx the table
###
#Order cols as you like
library(tibble)
Replicated_byus <- as_data_frame(Replicated_byus)
colnames(Replicated_byus)
#order the table by CHR and then P0
str(Replicated_byus)
Replicated_byus$CHR <- as.numeric(as.character(Replicated_byus$CHR))
Replicated_byus$P0 <- as.numeric(as.character(Replicated_byus$P0))
Replicated_byus$P1 <- as.numeric(as.character(Replicated_byus$P1))
Replicated_byus <- Replicated_byus[order(Replicated_byus$CHR, Replicated_byus$P0), ]
#fix columns
Replicated_byus$Location <- paste0('chr',Replicated_byus$CHR,':', Replicated_byus$P0,'-',Replicated_byus$P1)
Replicated_byus$DallAglio_Zscore <- Replicated_byus$TWAS.Z
Replicated_byus$DallAglio_pvalue <- Replicated_byus$TWAS.P
Replicated_byus$Jansen_pvalue_controlsvscurrent <- Replicated_byus$`P control vs current`
Replicated_byus$Jansen_zscore_controlsvscurrent <- Replicated_byus$`B control vs current`
#set a column order
col_order3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")
Replicated_byus <- Replicated_byus[, col_order3]
Replicated_byus
#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies
#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 28 #Bonf. significance = nominal p value / number of unique genes
# = 0.001785714
Replicated_byus$Bonf_validated <- ifelse(Replicated_byus$DallAglio_pvalue < 0.001785714, "Yes", "No")
#2) add the column specifying whether direction of effects is consistent
Replicated_byus$Consistent_dir_effect <- ifelse((Replicated_byus$Jansen_zscore_controlsvscurrent > 0 & Replicated_byus$DallAglio_Zscore > 0) | (Replicated_byus$Jansen_zscore_controlsvscurrent < 0 & Replicated_byus$DallAglio_Zscore <0), "Yes", "No")
sum(Replicated_byus$Consistent_dir_effect == "Yes") #26 associations present the same direction of effect
sum(Replicated_byus$Consistent_dir_effect == "No") #28 associations present a different direction of effect
sum(Replicated_byus$Bonf_validated == "Yes") #9 associations Bonferroni validated
print(Replicated_byus$ID[Replicated_byus$Bonf_validated == "Yes"])
# "PAPPA2" "MBNL1" "TMEM64" "TMEM64" "TMEM64" "TMEM64" "GNPTAB" "KTN1" "KTN1"
#these come from 5 unique genes = PAPPA2, MBNL1, TMEM64, GNPTAB, KTN1
#change col order again
col_order_3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")
Replicated_byus <- Replicated_byus[, col_order_3]
head(Replicated_byus)
#save as csv file
write.csv(Replicated_byus, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/Jansenvsourfindings_correct.csv', row.names = F)
Replicated_byusFindings 54 associations in the Jansen et al. (2016) study were replicated by our findings at the nominal level in any tissue, while 9 at a Bonferroni significance threshold. Of these, 28 and 5 were from unique genes, respectively. Such unique genes are PAPPA2, MBNL1, TMEM64, GNPTAB, KTN1. Of note, half of the feature associations presented the same direction of effect.
Report ended